Package google.cloud.aiplatform.v1beta1

Index

• AgentService (interface)
• DataFoundryService (interface)
• DatasetService (interface)
• DeploymentResourcePoolService (interface)
• EndpointService (interface)
• EvaluationAnalyticsService (interface)
• EvaluationService (interface)
• ExampleStoreService (interface)
• ExtensionExecutionService (interface)
• ExtensionRegistryService (interface)
• FeatureOnlineStoreAdminService (interface)
• FeatureOnlineStoreService (interface)
• FeatureRegistryService (interface)
• FeaturestoreOnlineServingService (interface)
• FeaturestoreService (interface)
• GenAiCacheService (interface)
• GenAiTuningService (interface)
• IndexEndpointService (interface)
• IndexService (interface)
• JobService (interface)
• LlmUtilityService (interface)
• MatchService (interface)
• MemoryBankService (interface)
• MetadataService (interface)
• MigrationService (interface)
• ModelGardenService (interface)
• ModelMonitoringService (interface)
• ModelService (interface)
• NotebookService (interface)
• OnlineEvaluatorService (interface)
• PersistentResourceService (interface)
• PipelineService (interface)
• PredictionService (interface)
• ReasoningEngineExecutionService (interface)
• ReasoningEngineRuntimeRevisionService (interface)
• ReasoningEngineService (interface)
• ScheduleService (interface)
• SessionService (interface)
• SkillRegistryService (interface)
• SpecialistPoolService (interface)
• TensorboardService (interface)
• VertexRagDataService (interface)
• VertexRagService (interface)
• VizierService (interface)
• AcceleratorType (enum)
• AcceptPublisherModelEulaRequest (message)
• ActivateOnlineEvaluatorOperationMetadata (message)
• ActivateOnlineEvaluatorRequest (message)
• AddContextArtifactsAndExecutionsRequest (message)
• AddContextArtifactsAndExecutionsResponse (message)
• AddContextChildrenRequest (message)
• AddContextChildrenResponse (message)
• AddExecutionEventsRequest (message)
• AddExecutionEventsResponse (message)
• AddTrialMeasurementRequest (message)
• Agent (message)
• AgentConfig (message)
• AgentData (message)
• AgentEvent (message)
• AgentTool (message)
• AggregationOutput (message)
• AggregationResult (message)
• Annotation (message)
• AnnotationSpec (message)
• ApiAuth (message)
• ApiAuth.ApiKeyConfig (message)
• AppendEventRequest (message)
• AppendEventResponse (message)
• Artifact (message)
• Artifact.State (enum)
• ArtifactTypeSchema (message)
• AskContextsRequest (message)
• AskContextsResponse (message)
• AssembleDataOperationMetadata (message)
• AssembleDataRequest (message)
• AssembleDataResponse (message)
• AssessDataOperationMetadata (message)
• AssessDataRequest (message)
• AssessDataRequest.BatchPredictionResourceUsageAssessmentConfig (message)
• AssessDataRequest.BatchPredictionValidationAssessmentConfig (message)
• AssessDataRequest.TuningResourceUsageAssessmentConfig (message)
• AssessDataRequest.TuningValidationAssessmentConfig (message)
• AssessDataRequest.TuningValidationAssessmentConfig.DatasetUsage (enum)
• AssessDataResponse (message)
• AssessDataResponse.BatchPredictionResourceUsageAssessmentResult (message)
• AssessDataResponse.BatchPredictionValidationAssessmentResult (message)
• AssessDataResponse.TuningResourceUsageAssessmentResult (message)
• AssessDataResponse.TuningValidationAssessmentResult (message)
• AssignNotebookRuntimeOperationMetadata (message)
• AssignNotebookRuntimeRequest (message)
• AsyncQueryReasoningEngineOperationMetadata (message)
• AsyncQueryReasoningEngineRequest (message)
• AsyncQueryReasoningEngineResponse (message)
• AsyncRetrieveContextsOperationMetadata (message)
• AsyncRetrieveContextsRequest (message)
• AsyncRetrieveContextsResponse (message)
• Attribution (message)
• AugmentPromptRequest (message)
• AugmentPromptRequest.Model (message)
• AugmentPromptResponse (message)
• AuthConfig (message)
• AuthConfig.ApiKeyConfig (message)
• AuthConfig.GoogleServiceAccountConfig (message)
• AuthConfig.HttpBasicAuthConfig (message)
• AuthConfig.OauthConfig (message)
• AuthConfig.OidcConfig (message)
• AuthType (enum)
• AutomaticResources (message)
• AutoraterConfig (message)
• AutoscalingMetricSpec (message)
• AvroSource (message)
• BatchCancelPipelineJobsOperationMetadata (message)
• BatchCancelPipelineJobsRequest (message)
• BatchCancelPipelineJobsResponse (message)
• BatchCreateFeaturesOperationMetadata (message)
• BatchCreateFeaturesRequest (message)
• BatchCreateFeaturesResponse (message)
• BatchCreateRagDataSchemasOperationMetadata (message)
• BatchCreateRagDataSchemasRequest (message)
• BatchCreateRagDataSchemasResponse (message)
• BatchCreateRagMetadataOperationMetadata (message)
• BatchCreateRagMetadataRequest (message)
• BatchCreateRagMetadataResponse (message)
• BatchCreateTensorboardRunsRequest (message)
• BatchCreateTensorboardRunsResponse (message)
• BatchCreateTensorboardTimeSeriesRequest (message)
• BatchCreateTensorboardTimeSeriesResponse (message)
• BatchDedicatedResources (message)
• BatchDeletePipelineJobsRequest (message)
• BatchDeletePipelineJobsResponse (message)
• BatchDeleteRagDataSchemasRequest (message)
• BatchDeleteRagMetadataRequest (message)
• BatchImportEvaluatedAnnotationsRequest (message)
• BatchImportEvaluatedAnnotationsResponse (message)
• BatchImportModelEvaluationSlicesRequest (message)
• BatchImportModelEvaluationSlicesResponse (message)
• BatchMigrateResourcesOperationMetadata (message)
• BatchMigrateResourcesOperationMetadata.PartialResult (message)
• BatchMigrateResourcesRequest (message)
• BatchMigrateResourcesResponse (message)
• BatchPredictionJob (message)
• BatchPredictionJob.InputConfig (message)
• BatchPredictionJob.InstanceConfig (message)
• BatchPredictionJob.OutputConfig (message)
• BatchPredictionJob.OutputInfo (message)
• BatchReadFeatureValuesOperationMetadata (message)
• BatchReadFeatureValuesRequest (message)
• BatchReadFeatureValuesRequest.EntityTypeSpec (message)
• BatchReadFeatureValuesRequest.PassThroughField (message)
• BatchReadFeatureValuesResponse (message)
• BatchReadTensorboardTimeSeriesDataRequest (message)
• BatchReadTensorboardTimeSeriesDataResponse (message)
• BidiInvokeReasoningEngineRequest (message)
• BigQueryDestination (message)
• BigQuerySource (message)
• BleuInput (message)
• BleuInstance (message)
• BleuMetricValue (message)
• BleuResults (message)
• BleuSpec (message)
• Blob (message)
• BlurBaselineConfig (message)
• BoolArray (message)
• CachedContent (message)
• CachedContent.UsageMetadata (message)
• CancelAsyncQueryReasoningEngineRequest (message)
• CancelAsyncQueryReasoningEngineResponse (message)
• CancelBatchPredictionJobRequest (message)
• CancelCustomJobRequest (message)
• CancelHyperparameterTuningJobRequest (message)
• CancelPipelineJobRequest (message)
• CancelTrainingPipelineRequest (message)
• CancelTuningJobRequest (message)
• Candidate (message)
• Candidate.FinishReason (enum)
• ChatCompletionsRequest (message)
• CheckPublisherModelEulaAcceptanceRequest (message)
• CheckTrialEarlyStoppingStateMetatdata (message)
• CheckTrialEarlyStoppingStateRequest (message)
• CheckTrialEarlyStoppingStateResponse (message)
• Checkpoint (message)
• Citation (message)
• CitationMetadata (message)
• Claim (message)
• ClientConnectionConfig (message)
• CodeExecutionResult (message)
• CodeExecutionResult.Outcome (enum)
• CoherenceInput (message)
• CoherenceInstance (message)
• CoherenceResult (message)
• CoherenceSpec (message)
• ColabImage (message)
• CometInput (message)
• CometInstance (message)
• CometResult (message)
• CometSpec (message)
• CometSpec.CometVersion (enum)
• CompleteTrialRequest (message)
• CompletionStats (message)
• ComputationBasedMetricSpec (message)
• ComputationBasedMetricSpec.ComputationBasedMetricType (enum)
• ComputeTokensRequest (message)
• ComputeTokensResponse (message)
• ContainerRegistryDestination (message)
• ContainerSpec (message)
• Content (message)
• ContentMap (message)
• ContentMap.Contents (message)
• ContentsExample (message)
• ContentsExample.ExpectedContent (message)
• Context (message)
• ConversationTurn (message)
• CopyModelOperationMetadata (message)
• CopyModelRequest (message)
• CopyModelResponse (message)
• CorpusStatus (message)
• CorpusStatus.State (enum)
• CorroborateContentRequest (message)
• CorroborateContentRequest.Parameters (message)
• CorroborateContentResponse (message)
• CountTokensRequest (message)
• CountTokensResponse (message)
• CreateAgentOperationMetadata (message)
• CreateAgentRequest (message)
• CreateArtifactRequest (message)
• CreateBatchPredictionJobRequest (message)
• CreateCachedContentRequest (message)
• CreateContextRequest (message)
• CreateCustomJobRequest (message)
• CreateDatasetOperationMetadata (message)
• CreateDatasetRequest (message)
• CreateDatasetVersionOperationMetadata (message)
• CreateDatasetVersionRequest (message)
• CreateDeploymentResourcePoolOperationMetadata (message)
• CreateDeploymentResourcePoolRequest (message)
• CreateEndpointOperationMetadata (message)
• CreateEndpointRequest (message)
• CreateEntityTypeOperationMetadata (message)
• CreateEntityTypeRequest (message)
• CreateExampleStoreOperationMetadata (message)
• CreateExampleStoreRequest (message)
• CreateExecutionRequest (message)
• CreateExtensionControllerOperationMetadata (message)
• CreateFeatureGroupOperationMetadata (message)
• CreateFeatureGroupRequest (message)
• CreateFeatureMonitorJobRequest (message)
• CreateFeatureMonitorOperationMetadata (message)
• CreateFeatureMonitorRequest (message)
• CreateFeatureOnlineStoreOperationMetadata (message)
• CreateFeatureOnlineStoreRequest (message)
• CreateFeatureOperationMetadata (message)
• CreateFeatureRequest (message)
• CreateFeatureViewOperationMetadata (message)
• CreateFeatureViewRequest (message)
• CreateFeaturestoreOperationMetadata (message)
• CreateFeaturestoreRequest (message)
• CreateHyperparameterTuningJobRequest (message)
• CreateIndexEndpointOperationMetadata (message)
• CreateIndexEndpointRequest (message)
• CreateIndexOperationMetadata (message)
• CreateIndexRequest (message)
• CreateMemoryOperationMetadata (message)
• CreateMemoryRequest (message)
• CreateMetadataSchemaRequest (message)
• CreateMetadataStoreOperationMetadata (message)
• CreateMetadataStoreRequest (message)
• CreateModelDeploymentMonitoringJobRequest (message)
• CreateModelMonitorOperationMetadata (message)
• CreateModelMonitorRequest (message)
• CreateModelMonitoringJobRequest (message)
• CreateNotebookExecutionJobOperationMetadata (message)
• CreateNotebookExecutionJobRequest (message)
• CreateNotebookRuntimeTemplateOperationMetadata (message)
• CreateNotebookRuntimeTemplateRequest (message)
• CreateOnlineEvaluatorOperationMetadata (message)
• CreateOnlineEvaluatorRequest (message)
• CreatePersistentResourceOperationMetadata (message)
• CreatePersistentResourceRequest (message)
• CreatePipelineJobRequest (message)
• CreateRagCorpusOperationMetadata (message)
• CreateRagCorpusRequest (message)
• CreateRagDataSchemaRequest (message)
• CreateRagMetadataRequest (message)
• CreateReasoningEngineOperationMetadata (message)
• CreateReasoningEngineRequest (message)
• CreateRegistryFeatureOperationMetadata (message)
• CreateScheduleRequest (message)
• CreateSessionOperationMetadata (message)
• CreateSessionRequest (message)
• CreateSkillOperationMetadata (message)
• CreateSkillRequest (message)
• CreateSolverOperationMetadata (message)
• CreateSpecialistPoolOperationMetadata (message)
• CreateSpecialistPoolRequest (message)
• CreateStudyRequest (message)
• CreateTensorboardExperimentRequest (message)
• CreateTensorboardOperationMetadata (message)
• CreateTensorboardRequest (message)
• CreateTensorboardRunRequest (message)
• CreateTensorboardTimeSeriesRequest (message)
• CreateTrainingPipelineRequest (message)
• CreateTrialRequest (message)
• CreateTuningJobRequest (message)
• CsvDestination (message)
• CsvSource (message)
• CustomCodeExecutionResult (message)
• CustomCodeExecutionSpec (message)
• CustomJob (message)
• CustomJobSpec (message)
• CustomOutput (message)
• CustomOutputFormatConfig (message)
• DataItem (message)
• DataItemView (message)
• Dataset (message)
• DatasetDistribution (message)
• DatasetDistribution.DistributionBucket (message)
• DatasetStats (message)
• DatasetVersion (message)
• DedicatedResources (message)
• DedicatedResources.ScaleToZeroSpec (message)
• DeleteAgentRequest (message)
• DeleteArtifactRequest (message)
• DeleteBatchPredictionJobRequest (message)
• DeleteCachedContentRequest (message)
• DeleteContextRequest (message)
• DeleteCustomJobRequest (message)
• DeleteDatasetRequest (message)
• DeleteDatasetVersionRequest (message)
• DeleteDeploymentResourcePoolRequest (message)
• DeleteEndpointRequest (message)
• DeleteEntityTypeRequest (message)
• DeleteExampleStoreOperationMetadata (message)
• DeleteExampleStoreRequest (message)
• DeleteExecutionRequest (message)
• DeleteExtensionRequest (message)
• DeleteFeatureGroupRequest (message)
• DeleteFeatureMonitorRequest (message)
• DeleteFeatureOnlineStoreRequest (message)
• DeleteFeatureRequest (message)
• DeleteFeatureValuesOperationMetadata (message)
• DeleteFeatureValuesRequest (message)
• DeleteFeatureValuesRequest.SelectEntity (message)
• DeleteFeatureValuesRequest.SelectTimeRangeAndFeature (message)
• DeleteFeatureValuesResponse (message)
• DeleteFeatureValuesResponse.SelectEntity (message)
• DeleteFeatureValuesResponse.SelectTimeRangeAndFeature (message)
• DeleteFeatureViewRequest (message)
• DeleteFeaturestoreRequest (message)
• DeleteHyperparameterTuningJobRequest (message)
• DeleteIndexEndpointRequest (message)
• DeleteIndexRequest (message)
• DeleteMemoryOperationMetadata (message)
• DeleteMemoryRequest (message)
• DeleteMetadataStoreOperationMetadata (message)
• DeleteMetadataStoreRequest (message)
• DeleteModelDeploymentMonitoringJobRequest (message)
• DeleteModelMonitorRequest (message)
• DeleteModelMonitoringJobRequest (message)
• DeleteModelRequest (message)
• DeleteModelVersionRequest (message)
• DeleteNotebookExecutionJobRequest (message)
• DeleteNotebookRuntimeRequest (message)
• DeleteNotebookRuntimeTemplateRequest (message)
• DeleteOnlineEvaluatorOperationMetadata (message)
• DeleteOnlineEvaluatorRequest (message)
• DeleteOperationMetadata (message)
• DeletePersistentResourceRequest (message)
• DeletePipelineJobRequest (message)
• DeleteRagCorpusRequest (message)
• DeleteRagDataSchemaRequest (message)
• DeleteRagFileRequest (message)
• DeleteRagMetadataRequest (message)
• DeleteReasoningEngineRequest (message)
• DeleteReasoningEngineRuntimeRevisionOperationMetadata (message)
• DeleteReasoningEngineRuntimeRevisionRequest (message)
• DeleteResponseRequest (message)
• DeleteSavedQueryRequest (message)
• DeleteScheduleRequest (message)
• DeleteSessionRequest (message)
• DeleteSkillOperationMetadata (message)
• DeleteSkillRequest (message)
• DeleteSpecialistPoolRequest (message)
• DeleteStudyRequest (message)
• DeleteTensorboardExperimentRequest (message)
• DeleteTensorboardRequest (message)
• DeleteTensorboardRunRequest (message)
• DeleteTensorboardTimeSeriesRequest (message)
• DeleteTrainingPipelineRequest (message)
• DeleteTrialRequest (message)
• DeployIndexOperationMetadata (message)
• DeployIndexRequest (message)
• DeployIndexResponse (message)
• DeployModelOperationMetadata (message)
• DeployModelRequest (message)
• DeployModelResponse (message)
• DeployOperationMetadata (message)
• DeployPublisherModelOperationMetadata (message) (deprecated)
• DeployPublisherModelRequest (message) (deprecated)
• DeployPublisherModelResponse (message) (deprecated)
• DeployRequest (message)
• DeployRequest.CustomModel (message)
• DeployRequest.DeployConfig (message)
• DeployRequest.EndpointConfig (message)
• DeployRequest.ModelConfig (message)
• DeployResponse (message)
• DeploySolverOperationMetadata (message)
• DeployedIndex (message)
• DeployedIndex.DeploymentTier (enum)
• DeployedIndexAuthConfig (message)
• DeployedIndexAuthConfig.AuthProvider (message)
• DeployedIndexRef (message)
• DeployedModel (message)
• DeployedModel.Status (message)
• DeployedModelRef (message)
• DeploymentResourcePool (message)
• DeploymentStage (enum)
• DestinationFeatureSetting (message)
• DirectPredictRequest (message)
• DirectPredictResponse (message)
• DirectRawPredictRequest (message)
• DirectRawPredictResponse (message)
• DirectUploadSource (message)
• DiskSpec (message)
• DistillationDataStats (message)
• DistillationHyperParameters (message)
• DistillationSpec (message)
• DnsPeeringConfig (message)
• DoubleArray (message)
• DynamicRetrievalConfig (message)
• DynamicRetrievalConfig.Mode (enum)
• EmbedContentRequest (message)
• EmbedContentRequest.EmbedContentConfig (message)
• EmbedContentRequest.EmbeddingTaskType (enum)
• EmbedContentResponse (message)
• EmbedContentResponse.Embedding (message)
• EncryptionSpec (message)
• Endpoint (message)
• EnterpriseWebSearch (message)
• EntityIdSelector (message)
• EntityType (message)
• EnvVar (message)
• ErrorAnalysisAnnotation (message)
• ErrorAnalysisAnnotation.AttributedItem (message)
• ErrorAnalysisAnnotation.QueryType (enum)
• EvaluateDatasetOperationMetadata (message)
• EvaluateDatasetRequest (message)
• EvaluateDatasetResponse (message)
• EvaluateDatasetRun (message)
• EvaluateInstancesRequest (message)
• EvaluateInstancesResponse (message)
• EvaluatedAnnotation (message)
• EvaluatedAnnotation.EvaluatedAnnotationType (enum)
• EvaluatedAnnotationExplanation (message)
• EvaluationConfig (message)
• EvaluationDataset (message)
• EvaluationInstance (message)
• EvaluationInstance.DeprecatedAgentConfig (message) (deprecated)
• EvaluationInstance.DeprecatedAgentConfig.Tools (message)
• EvaluationInstance.DeprecatedAgentData (message) (deprecated)
• EvaluationInstance.DeprecatedAgentData.AgentEvent (message)
• EvaluationInstance.DeprecatedAgentData.ConversationTurn (message)
• EvaluationInstance.DeprecatedAgentData.Events (message)
• EvaluationInstance.DeprecatedAgentData.Tools (message)
• EvaluationInstance.InstanceData (message)
• EvaluationInstance.InstanceData.Contents (message)
• EvaluationInstance.MapInstance (message)
• EvaluationParserConfig (message)
• EvaluationParserConfig.CustomCodeParserConfig (message)
• Event (message)
• Event.Type (enum)
• EventActions (message)
• EventMetadata (message)
• ExactMatchInput (message)
• ExactMatchInstance (message)
• ExactMatchMetricValue (message)
• ExactMatchResults (message)
• ExactMatchSpec (message)
• Example (message)
• ExampleStore (message)
• ExampleStoreConfig (message)
• Examples (message)
• Examples.ExampleGcsSource (message)
• Examples.ExampleGcsSource.DataFormat (enum)
• ExamplesArrayFilter (message)
• ExamplesArrayFilter.ArrayOperator (enum)
• ExamplesOverride (message)
• ExamplesOverride.DataFormat (enum)
• ExamplesRestrictionsNamespace (message)
• ExecutableCode (message)
• ExecutableCode.Language (enum)
• ExecuteExtensionRequest (message)
• ExecuteExtensionResponse (message)
• Execution (message)
• Execution.State (enum)
• ExplainRequest (message)
• ExplainResponse (message)
• ExplainResponse.ConcurrentExplanation (message)
• Explanation (message)
• ExplanationMetadata (message)
• ExplanationMetadata.InputMetadata (message)
• ExplanationMetadata.InputMetadata.Encoding (enum)
• ExplanationMetadata.InputMetadata.FeatureValueDomain (message)
• ExplanationMetadata.InputMetadata.Visualization (message)
• ExplanationMetadata.InputMetadata.Visualization.ColorMap (enum)
• ExplanationMetadata.InputMetadata.Visualization.OverlayType (enum)
• ExplanationMetadata.InputMetadata.Visualization.Polarity (enum)
• ExplanationMetadata.InputMetadata.Visualization.Type (enum)
• ExplanationMetadata.OutputMetadata (message)
• ExplanationMetadataOverride (message)
• ExplanationMetadataOverride.InputMetadataOverride (message)
• ExplanationParameters (message)
• ExplanationSpec (message)
• ExplanationSpecOverride (message)
• ExportDataConfig (message)
• ExportDataOperationMetadata (message)
• ExportDataRequest (message)
• ExportDataResponse (message)
• ExportFeatureValuesOperationMetadata (message)
• ExportFeatureValuesRequest (message)
• ExportFeatureValuesRequest.FullExport (message)
• ExportFeatureValuesRequest.SnapshotExport (message)
• ExportFeatureValuesResponse (message)
• ExportFractionSplit (message)
• ExportModelOperationMetadata (message)
• ExportModelOperationMetadata.OutputInfo (message)
• ExportModelRequest (message)
• ExportModelRequest.OutputConfig (message)
• ExportModelResponse (message)
• ExportPublisherModelOperationMetadata (message)
• ExportPublisherModelRequest (message)
• ExportPublisherModelResponse (message)
• ExportTensorboardTimeSeriesDataRequest (message)
• ExportTensorboardTimeSeriesDataResponse (message)
• Extension (message)
• ExtensionManifest (message)
• ExtensionManifest.ApiSpec (message)
• ExtensionOperation (message)
• ExtensionPrivateServiceConnectConfig (message)
• Fact (message)
• FailedRubric (message)
• FasterDeploymentConfig (message)
• Feature (message)
• Feature.MonitoringStatsAnomaly (message)
• Feature.MonitoringStatsAnomaly.Objective (enum)
• Feature.ValueType (enum)
• FeatureGroup (message)
• FeatureGroup.BigQuery (message)
• FeatureGroup.BigQuery.TimeSeries (message)
• FeatureGroup.ServiceAgentType (enum)
• FeatureMonitor (message)
• FeatureMonitorJob (message)
• FeatureMonitorJob.FeatureMonitorJobTrigger (enum)
• FeatureMonitorJob.JobSummary (message)
• FeatureNoiseSigma (message)
• FeatureNoiseSigma.NoiseSigmaForFeature (message)
• FeatureOnlineStore (message)
• FeatureOnlineStore.Bigtable (message)
• FeatureOnlineStore.Bigtable.AutoScaling (message)
• FeatureOnlineStore.Bigtable.BigtableMetadata (message)
• FeatureOnlineStore.DedicatedServingEndpoint (message)
• FeatureOnlineStore.EmbeddingManagement (message) (deprecated)
• FeatureOnlineStore.Optimized (message)
• FeatureOnlineStore.State (enum)
• FeatureSelectionConfig (message)
• FeatureSelectionConfig.FeatureConfig (message)
• FeatureSelector (message)
• FeatureStatsAndAnomaly (message)
• FeatureStatsAndAnomalySpec (message)
• FeatureStatsAnomaly (message)
• FeatureValue (message)
• FeatureValue.Metadata (message)
• FeatureValueDestination (message)
• FeatureValueList (message)
• FeatureView (message)
• FeatureView.BigQuerySource (message)
• FeatureView.BigtableMetadata (message)
• FeatureView.FeatureRegistrySource (message)
• FeatureView.FeatureRegistrySource.FeatureGroup (message)
• FeatureView.IndexConfig (message)
• FeatureView.IndexConfig.BruteForceConfig (message)
• FeatureView.IndexConfig.DistanceMeasureType (enum)
• FeatureView.IndexConfig.TreeAHConfig (message)
• FeatureView.OptimizedConfig (message)
• FeatureView.ServiceAgentType (enum)
• FeatureView.SyncConfig (message)
• FeatureView.VectorSearchConfig (message) (deprecated)
• FeatureView.VectorSearchConfig.BruteForceConfig (message)
• FeatureView.VectorSearchConfig.DistanceMeasureType (enum)
• FeatureView.VectorSearchConfig.TreeAHConfig (message)
• FeatureView.VertexRagSource (message)
• FeatureViewDataFormat (enum)
• FeatureViewDataKey (message)
• FeatureViewDataKey.CompositeKey (message)
• FeatureViewDirectWriteRequest (message)
• FeatureViewDirectWriteRequest.DataKeyAndFeatureValues (message)
• FeatureViewDirectWriteRequest.DataKeyAndFeatureValues.Feature (message)
• FeatureViewDirectWriteRequest.DataKeyAndFeatureValues.Feature.FeatureValueAndTimestamp (message)
• FeatureViewDirectWriteResponse (message)
• FeatureViewDirectWriteResponse.WriteResponse (message)
• FeatureViewSync (message)
• FeatureViewSync.SyncSummary (message)
• Featurestore (message)
• Featurestore.OnlineServingConfig (message)
• Featurestore.OnlineServingConfig.Scaling (message)
• Featurestore.State (enum)
• FeaturestoreMonitoringConfig (message)
• FeaturestoreMonitoringConfig.ImportFeaturesAnalysis (message)
• FeaturestoreMonitoringConfig.ImportFeaturesAnalysis.Baseline (enum)
• FeaturestoreMonitoringConfig.ImportFeaturesAnalysis.State (enum)
• FeaturestoreMonitoringConfig.SnapshotAnalysis (message)
• FeaturestoreMonitoringConfig.ThresholdConfig (message)
• FetchExamplesRequest (message)
• FetchExamplesResponse (message)
• FetchFeatureValuesRequest (message)
• FetchFeatureValuesRequest.Format (enum) (deprecated)
• FetchFeatureValuesResponse (message)
• FetchFeatureValuesResponse.FeatureNameValuePairList (message)
• FetchFeatureValuesResponse.FeatureNameValuePairList.FeatureNameValuePair (message)
• FetchPublisherModelConfigRequest (message)
• FileData (message)
• FileStatus (message)
• FileStatus.State (enum)
• FilterSplit (message)
• FlexStart (message)
• FluencyInput (message)
• FluencyInstance (message)
• FluencyResult (message)
• FluencySpec (message)
• FractionSplit (message)
• FulfillmentInput (message)
• FulfillmentInstance (message)
• FulfillmentResult (message)
• FulfillmentSpec (message)
• FullFineTunedResources (message)
• FullFineTunedResources.DeploymentType (enum)
• FunctionCall (message)
• FunctionCallingConfig (message)
• FunctionCallingConfig.Mode (enum)
• FunctionDeclaration (message)
• FunctionResponse (message)
• FunctionResponseBlob (message)
• FunctionResponseFileData (message)
• FunctionResponsePart (message)
• GcsDestination (message)
• GcsSource (message)
• GeminiExample (message)
• GeminiRequestReadConfig (message)
• GeminiTemplateConfig (message)
• GenAiAdvancedFeaturesConfig (message)
• GenAiAdvancedFeaturesConfig.RagConfig (message)
• GenerateContentRequest (message)
• GenerateContentResponse (message)
• GenerateContentResponse.PromptFeedback (message)
• GenerateContentResponse.PromptFeedback.BlockedReason (enum)
• GenerateContentResponse.UsageMetadata (message)
• GenerateContentResponse.UsageMetadata.TrafficType (enum)
• GenerateFetchAccessTokenRequest (message)
• GenerateFetchAccessTokenResponse (message)
• GenerateInstanceRubricsRequest (message)
• GenerateInstanceRubricsResponse (message)
• GenerateLossClustersOperationMetadata (message)
• GenerateLossClustersRequest (message)
• GenerateLossClustersRequest.EvaluationResultList (message)
• GenerateLossClustersResponse (message)
• GenerateMemoriesOperationMetadata (message)
• GenerateMemoriesRequest (message)
• GenerateMemoriesRequest.DirectContentsSource (message)
• GenerateMemoriesRequest.DirectContentsSource.Event (message)
• GenerateMemoriesRequest.DirectMemoriesSource (message)
• GenerateMemoriesRequest.DirectMemoriesSource.DirectMemory (message)
• GenerateMemoriesRequest.VertexSessionSource (message)
• GenerateMemoriesResponse (message)
• GenerateMemoriesResponse.GeneratedMemory (message)
• GenerateMemoriesResponse.GeneratedMemory.Action (enum)
• GenerateSyntheticDataRequest (message)
• GenerateSyntheticDataResponse (message)
• GenerateVideoResponse (message)
• GenerationConfig (message)
• GenerationConfig.MediaResolution (enum)
• GenerationConfig.Modality (enum)
• GenerationConfig.ModelConfig (message)
• GenerationConfig.ModelConfig.FeatureSelectionPreference (enum)
• GenerationConfig.RoutingConfig (message)
• GenerationConfig.RoutingConfig.AutoRoutingMode (message)
• GenerationConfig.RoutingConfig.AutoRoutingMode.ModelRoutingPreference (enum)
• GenerationConfig.RoutingConfig.ManualRoutingMode (message)
• GenerationConfig.ThinkingConfig (message)
• GenerationConfig.ThinkingConfig.ThinkingLevel (enum)
• GenericOperationMetadata (message)
• GenieSource (message)
• GetAgentRequest (message)
• GetAnnotationSpecRequest (message)
• GetArtifactRequest (message)
• GetBatchPredictionJobRequest (message)
• GetCachedContentRequest (message)
• GetContextRequest (message)
• GetCustomJobRequest (message)
• GetDatasetRequest (message)
• GetDatasetVersionRequest (message)
• GetDeploymentResourcePoolRequest (message)
• GetEndpointRequest (message)
• GetEntityTypeRequest (message)
• GetExampleStoreRequest (message)
• GetExecutionRequest (message)
• GetExtensionRequest (message)
• GetFeatureGroupRequest (message)
• GetFeatureMonitorJobRequest (message)
• GetFeatureMonitorRequest (message)
• GetFeatureOnlineStoreRequest (message)
• GetFeatureRequest (message)
• GetFeatureViewRequest (message)
• GetFeatureViewSyncRequest (message)
• GetFeaturestoreRequest (message)
• GetHyperparameterTuningJobRequest (message)
• GetIndexEndpointRequest (message)
• GetIndexRequest (message)
• GetMemoryRequest (message)
• GetMetadataSchemaRequest (message)
• GetMetadataStoreRequest (message)
• GetModelDeploymentMonitoringJobRequest (message)
• GetModelEvaluationRequest (message)
• GetModelEvaluationSliceRequest (message)
• GetModelMonitorRequest (message)
• GetModelMonitoringJobRequest (message)
• GetModelRequest (message)
• GetNotebookExecutionJobRequest (message)
• GetNotebookRuntimeRequest (message)
• GetNotebookRuntimeTemplateRequest (message)
• GetOnlineEvaluatorRequest (message)
• GetPersistentResourceRequest (message)
• GetPipelineJobRequest (message)
• GetPublisherModelRequest (message)
• GetRagCorpusRequest (message)
• GetRagDataSchemaRequest (message)
• GetRagEngineConfigRequest (message)
• GetRagFileRequest (message)
• GetRagMetadataRequest (message)
• GetReasoningEngineRequest (message)
• GetReasoningEngineRuntimeRevisionRequest (message)
• GetResponseRequest (message)
• GetScheduleRequest (message)
• GetSessionRequest (message)
• GetSkillRequest (message)
• GetSkillRevisionRequest (message)
• GetSpecialistPoolRequest (message)
• GetStudyRequest (message)
• GetTensorboardExperimentRequest (message)
• GetTensorboardRequest (message)
• GetTensorboardRunRequest (message)
• GetTensorboardTimeSeriesRequest (message)
• GetTrainingPipelineRequest (message)
• GetTrialRequest (message)
• GetTuningJobRequest (message)
• GoogleDriveSource (message)
• GoogleDriveSource.ResourceId (message)
• GoogleDriveSource.ResourceId.ResourceType (enum)
• GoogleMaps (message)
• GoogleSearchRetrieval (message)
• GroundednessInput (message)
• GroundednessInstance (message)
• GroundednessResult (message)
• GroundednessSpec (message)
• GroundingChunk (message)
• GroundingChunk.Maps (message)
• GroundingChunk.Maps.PlaceAnswerSources (message)
• GroundingChunk.Maps.PlaceAnswerSources.ReviewSnippet (message)
• GroundingChunk.RetrievedContext (message)
• GroundingChunk.Web (message)
• GroundingMetadata (message)
• GroundingMetadata.SourceFlaggingUri (message)
• GroundingSupport (message)
• HarmCategory (enum)
• HttpElementLocation (enum)
• HyperparameterTuningJob (message)
• IdMatcher (message)
• ImageConfig (message)
• ImageConfig.ImageOutputOptions (message)
• ImageConfig.PersonGeneration (enum)
• ImportDataConfig (message)
• ImportDataOperationMetadata (message)
• ImportDataRequest (message)
• ImportDataResponse (message)
• ImportExtensionOperationMetadata (message)
• ImportExtensionRequest (message)
• ImportFeatureValuesOperationMetadata (message)
• ImportFeatureValuesRequest (message)
• ImportFeatureValuesRequest.FeatureSpec (message)
• ImportFeatureValuesResponse (message)
• ImportIndexOperationMetadata (message)
• ImportIndexRequest (message)
• ImportIndexRequest.ConnectorConfig (message)
• ImportIndexRequest.ConnectorConfig.BigQuerySourceConfig (message)
• ImportIndexRequest.ConnectorConfig.DatapointFieldMapping (message)
• ImportIndexRequest.ConnectorConfig.DatapointFieldMapping.NumericRestrict (message)
• ImportIndexRequest.ConnectorConfig.DatapointFieldMapping.NumericRestrict.ValueType (enum)
• ImportIndexRequest.ConnectorConfig.DatapointFieldMapping.Restrict (message)
• ImportModelEvaluationRequest (message)
• ImportRagFilesConfig (message)
• ImportRagFilesOperationMetadata (message)
• ImportRagFilesRequest (message)
• ImportRagFilesResponse (message)
• Index (message)
• Index.IndexUpdateMethod (enum)
• IndexDatapoint (message)
• IndexDatapoint.CrowdingTag (message)
• IndexDatapoint.NumericRestriction (message)
• IndexDatapoint.NumericRestriction.Operator (enum)
• IndexDatapoint.Restriction (message)
• IndexDatapoint.SparseEmbedding (message)
• IndexEndpoint (message)
• IndexPrivateEndpoints (message)
• IndexStats (message)
• IngestEventsMetadata (message)
• IngestEventsRequest (message)
• IngestEventsResponse (message)
• IngestionDirectContentsSource (message)
• IngestionDirectContentsSource.Event (message)
• InputDataConfig (message)
• Int64Array (message)
• IntegratedGradientsAttribution (message)
• InvokeReasoningEngineRequest (message)
• JiraSource (message)
• JiraSource.JiraQueries (message)
• JobState (enum)
• KeepAliveProbe (message)
• KeepAliveProbe.HttpGet (message)
• LLMBasedMetricSpec (message)
• LargeModelReference (message)
• LineageSubgraph (message)
• ListAgentsRequest (message)
• ListAgentsResponse (message)
• ListAnnotationsRequest (message)
• ListAnnotationsResponse (message)
• ListArtifactsRequest (message)
• ListArtifactsResponse (message)
• ListBatchPredictionJobsRequest (message)
• ListBatchPredictionJobsResponse (message)
• ListCachedContentsRequest (message)
• ListCachedContentsResponse (message)
• ListContextsRequest (message)
• ListContextsResponse (message)
• ListCustomJobsRequest (message)
• ListCustomJobsResponse (message)
• ListDataItemsRequest (message)
• ListDataItemsResponse (message)
• ListDatasetVersionsRequest (message)
• ListDatasetVersionsResponse (message)
• ListDatasetsRequest (message)
• ListDatasetsResponse (message)
• ListDeploymentResourcePoolsRequest (message)
• ListDeploymentResourcePoolsResponse (message)
• ListEndpointsRequest (message)
• ListEndpointsResponse (message)
• ListEntityTypesRequest (message)
• ListEntityTypesResponse (message)
• ListEventsRequest (message)
• ListEventsResponse (message)
• ListExampleStoresRequest (message)
• ListExampleStoresResponse (message)
• ListExecutionsRequest (message)
• ListExecutionsResponse (message)
• ListExtensionsRequest (message)
• ListExtensionsResponse (message)
• ListFeatureGroupsRequest (message)
• ListFeatureGroupsResponse (message)
• ListFeatureMonitorJobsRequest (message)
• ListFeatureMonitorJobsResponse (message)
• ListFeatureMonitorsRequest (message)
• ListFeatureMonitorsResponse (message)
• ListFeatureOnlineStoresRequest (message)
• ListFeatureOnlineStoresResponse (message)
• ListFeatureViewSyncsRequest (message)
• ListFeatureViewSyncsResponse (message)
• ListFeatureViewsRequest (message)
• ListFeatureViewsResponse (message)
• ListFeaturesRequest (message)
• ListFeaturesResponse (message)
• ListFeaturestoresRequest (message)
• ListFeaturestoresResponse (message)
• ListHyperparameterTuningJobsRequest (message)
• ListHyperparameterTuningJobsResponse (message)
• ListIndexEndpointsRequest (message)
• ListIndexEndpointsResponse (message)
• ListIndexesRequest (message)
• ListIndexesResponse (message)
• ListMemoriesRequest (message)
• ListMemoriesResponse (message)
• ListMetadataSchemasRequest (message)
• ListMetadataSchemasResponse (message)
• ListMetadataStoresRequest (message)
• ListMetadataStoresResponse (message)
• ListModelDeploymentMonitoringJobsRequest (message)
• ListModelDeploymentMonitoringJobsResponse (message)
• ListModelEvaluationSlicesRequest (message)
• ListModelEvaluationSlicesResponse (message)
• ListModelEvaluationsRequest (message)
• ListModelEvaluationsResponse (message)
• ListModelMonitoringJobsRequest (message)
• ListModelMonitoringJobsResponse (message)
• ListModelMonitorsRequest (message)
• ListModelMonitorsResponse (message)
• ListModelVersionCheckpointsRequest (message)
• ListModelVersionCheckpointsResponse (message)
• ListModelVersionsRequest (message)
• ListModelVersionsResponse (message)
• ListModelsRequest (message)
• ListModelsResponse (message)
• ListNotebookExecutionJobsRequest (message)
• ListNotebookExecutionJobsResponse (message)
• ListNotebookRuntimeTemplatesRequest (message)
• ListNotebookRuntimeTemplatesResponse (message)
• ListNotebookRuntimesRequest (message)
• ListNotebookRuntimesResponse (message)
• ListOnlineEvaluatorsRequest (message)
• ListOnlineEvaluatorsResponse (message)
• ListOptimalTrialsRequest (message)
• ListOptimalTrialsResponse (message)
• ListPersistentResourcesRequest (message)
• ListPersistentResourcesResponse (message)
• ListPipelineJobsRequest (message)
• ListPipelineJobsResponse (message)
• ListPublisherModelsRequest (message)
• ListPublisherModelsResponse (message)
• ListRagCorporaRequest (message)
• ListRagCorporaResponse (message)
• ListRagDataSchemasRequest (message)
• ListRagDataSchemasResponse (message)
• ListRagFilesRequest (message)
• ListRagFilesResponse (message)
• ListRagMetadataRequest (message)
• ListRagMetadataResponse (message)
• ListReasoningEngineRuntimeRevisionsRequest (message)
• ListReasoningEngineRuntimeRevisionsResponse (message)
• ListReasoningEnginesRequest (message)
• ListReasoningEnginesResponse (message)
• ListSavedQueriesRequest (message)
• ListSavedQueriesResponse (message)
• ListSchedulesRequest (message)
• ListSchedulesResponse (message)
• ListSessionsRequest (message)
• ListSessionsResponse (message)
• ListSkillRevisionsRequest (message)
• ListSkillRevisionsResponse (message)
• ListSkillsRequest (message)
• ListSkillsResponse (message)
• ListSpecialistPoolsRequest (message)
• ListSpecialistPoolsResponse (message)
• ListStudiesRequest (message)
• ListStudiesResponse (message)
• ListTensorboardExperimentsRequest (message)
• ListTensorboardExperimentsResponse (message)
• ListTensorboardRunsRequest (message)
• ListTensorboardRunsResponse (message)
• ListTensorboardTimeSeriesRequest (message)
• ListTensorboardTimeSeriesResponse (message)
• ListTensorboardsRequest (message)
• ListTensorboardsResponse (message)
• ListTrainingPipelinesRequest (message)
• ListTrainingPipelinesResponse (message)
• ListTrialsRequest (message)
• ListTrialsResponse (message)
• ListTuningJobsRequest (message)
• ListTuningJobsResponse (message)
• LogprobsResult (message)
• LogprobsResult.Candidate (message)
• LogprobsResult.TopCandidates (message)
• LookupStudyRequest (message)
• LossAnalysisConfig (message)
• LossAnalysisResult (message)
• LossCluster (message)
• LossExample (message)
• LossTaxonomyEntry (message)
• LustreMount (message)
• MachineSpec (message)
• ManualBatchTuningParameters (message)
• Measurement (message)
• Measurement.Metric (message)
• Memory (message)
• Memory.StructuredContent (message)
• MemoryGenerationTriggerConfig (message)
• MemoryGenerationTriggerConfig.GenerationTriggerRule (message)
• MemoryProfile (message)
• MemoryType (enum)
• MergeVersionAliasesRequest (message)
• MetadataList (message)
• MetadataSchema (message)
• MetadataSchema.MetadataSchemaType (enum)
• MetadataStore (message)
• MetadataStore.DataplexConfig (message)
• MetadataStore.MetadataStoreState (message)
• MetadataValue (message)
• Metric (message)
• Metric.AggregationMetric (enum)
• MetricMetadata (message)
• MetricMetadata.ScoreRange (message)
• MetricResult (message)
• MetricSource (message)
• MetricxInput (message)
• MetricxInstance (message)
• MetricxResult (message)
• MetricxSpec (message)
• MetricxSpec.MetricxVersion (enum)
• MigratableResource (message)
• MigratableResource.AutomlDataset (message)
• MigratableResource.AutomlModel (message)
• MigratableResource.DataLabelingDataset (message)
• MigratableResource.DataLabelingDataset.DataLabelingAnnotatedDataset (message)
• MigratableResource.MlEngineModelVersion (message)
• MigrateResourceRequest (message)
• MigrateResourceRequest.MigrateAutomlDatasetConfig (message)
• MigrateResourceRequest.MigrateAutomlModelConfig (message)
• MigrateResourceRequest.MigrateDataLabelingDatasetConfig (message)
• MigrateResourceRequest.MigrateDataLabelingDatasetConfig.MigrateDataLabelingAnnotatedDatasetConfig (message)
• MigrateResourceRequest.MigrateMlEngineModelVersionConfig (message)
• MigrateResourceResponse (message)
• Modality (enum)
• ModalityTokenCount (message)
• Model (message)
• Model.BaseModelSource (message)
• Model.DeploymentResourcesType (enum)
• Model.ExportFormat (message)
• Model.ExportFormat.ExportableContent (enum)
• Model.OriginalModelInfo (message)
• ModelArmorConfig (message)
• ModelContainerSpec (message)
• ModelDeploymentMonitoringBigQueryTable (message)
• ModelDeploymentMonitoringBigQueryTable.LogSource (enum)
• ModelDeploymentMonitoringBigQueryTable.LogType (enum)
• ModelDeploymentMonitoringJob (message)
• ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata (message)
• ModelDeploymentMonitoringJob.MonitoringScheduleState (enum)
• ModelDeploymentMonitoringObjectiveConfig (message)
• ModelDeploymentMonitoringObjectiveType (enum)
• ModelDeploymentMonitoringScheduleConfig (message)
• ModelEvaluation (message)
• ModelEvaluation.BiasConfig (message)
• ModelEvaluation.ModelEvaluationExplanationSpec (message)
• ModelEvaluationSlice (message)
• ModelEvaluationSlice.Slice (message)
• ModelEvaluationSlice.Slice.SliceSpec (message)
• ModelEvaluationSlice.Slice.SliceSpec.Range (message)
• ModelEvaluationSlice.Slice.SliceSpec.SliceConfig (message)
• ModelEvaluationSlice.Slice.SliceSpec.Value (message)
• ModelExplanation (message)
• ModelGardenSource (message)
• ModelMonitor (message)
• ModelMonitor.ModelMonitoringTarget (message)
• ModelMonitor.ModelMonitoringTarget.VertexModelSource (message)
• ModelMonitoringAlert (message)
• ModelMonitoringAlertCondition (message)
• ModelMonitoringAlertConfig (message)
• ModelMonitoringAlertConfig.EmailAlertConfig (message)
• ModelMonitoringAnomaly (message)
• ModelMonitoringAnomaly.TabularAnomaly (message)
• ModelMonitoringConfig (message)
• ModelMonitoringInput (message)
• ModelMonitoringInput.BatchPredictionOutput (message)
• ModelMonitoringInput.ModelMonitoringDataset (message)
• ModelMonitoringInput.ModelMonitoringDataset.ModelMonitoringBigQuerySource (message)
• ModelMonitoringInput.ModelMonitoringDataset.ModelMonitoringGcsSource (message)
• ModelMonitoringInput.ModelMonitoringDataset.ModelMonitoringGcsSource.DataFormat (enum)
• ModelMonitoringInput.TimeOffset (message)
• ModelMonitoringInput.VertexEndpointLogs (message)
• ModelMonitoringJob (message)
• ModelMonitoringJobExecutionDetail (message)
• ModelMonitoringJobExecutionDetail.ProcessedDataset (message)
• ModelMonitoringNotificationSpec (message)
• ModelMonitoringNotificationSpec.EmailConfig (message)
• ModelMonitoringNotificationSpec.NotificationChannelConfig (message)
• ModelMonitoringObjectiveConfig (message)
• ModelMonitoringObjectiveConfig.ExplanationConfig (message)
• ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline (message)
• ModelMonitoringObjectiveConfig.ExplanationConfig.ExplanationBaseline.PredictionFormat (enum)
• ModelMonitoringObjectiveConfig.PredictionDriftDetectionConfig (message)
• ModelMonitoringObjectiveConfig.TrainingDataset (message)
• ModelMonitoringObjectiveConfig.TrainingPredictionSkewDetectionConfig (message)
• ModelMonitoringObjectiveSpec (message)
• ModelMonitoringObjectiveSpec.DataDriftSpec (message)
• ModelMonitoringObjectiveSpec.FeatureAttributionSpec (message)
• ModelMonitoringObjectiveSpec.TabularObjective (message)
• ModelMonitoringOutputSpec (message)
• ModelMonitoringSchema (message)
• ModelMonitoringSchema.FieldSchema (message)
• ModelMonitoringSpec (message)
• ModelMonitoringStats (message)
• ModelMonitoringStatsAnomalies (message)
• ModelMonitoringStatsAnomalies.FeatureHistoricStatsAnomalies (message)
• ModelMonitoringStatsDataPoint (message)
• ModelMonitoringStatsDataPoint.TypedValue (message)
• ModelMonitoringStatsDataPoint.TypedValue.DistributionDataValue (message)
• ModelMonitoringTabularStats (message)
• ModelSourceInfo (message)
• ModelSourceInfo.ModelSourceType (enum)
• ModelVersionCheckpoint (message) (deprecated)
• MultiSpeakerVoiceConfig (message)
• MutateDeployedIndexOperationMetadata (message)
• MutateDeployedIndexRequest (message)
• MutateDeployedIndexResponse (message)
• MutateDeployedModelOperationMetadata (message)
• MutateDeployedModelRequest (message)
• MutateDeployedModelResponse (message)
• NearestNeighborQuery (message)
• NearestNeighborQuery.Embedding (message)
• NearestNeighborQuery.NumericFilter (message)
• NearestNeighborQuery.NumericFilter.Operator (enum)
• NearestNeighborQuery.Parameters (message)
• NearestNeighborQuery.StringFilter (message)
• NearestNeighborSearchOperationMetadata (message)
• NearestNeighborSearchOperationMetadata.ContentValidationStats (message)
• NearestNeighborSearchOperationMetadata.RecordError (message)
• NearestNeighborSearchOperationMetadata.RecordError.RecordErrorType (enum)
• NearestNeighbors (message)
• NearestNeighbors.Neighbor (message)
• Neighbor (message)
• NetworkSpec (message)
• NfsMount (message)
• NotebookEucConfig (message)
• NotebookExecutionJob (message)
• NotebookExecutionJob.CustomEnvironmentSpec (message)
• NotebookExecutionJob.DataformRepositorySource (message)
• NotebookExecutionJob.DirectNotebookSource (message)
• NotebookExecutionJob.GcsNotebookSource (message)
• NotebookExecutionJob.WorkbenchRuntime (message)
• NotebookExecutionJobView (enum)
• NotebookIdleShutdownConfig (message)
• NotebookRuntime (message)
• NotebookRuntime.HealthState (enum)
• NotebookRuntime.RuntimeState (enum)
• NotebookRuntimeTemplate (message)
• NotebookRuntimeTemplateRef (message)
• NotebookRuntimeType (enum)
• NotebookSoftwareConfig (message)
• OnlineEvaluator (message)
• OnlineEvaluator.CloudObservability (message)
• OnlineEvaluator.CloudObservability.NumericPredicate (message)
• OnlineEvaluator.CloudObservability.NumericPredicate.ComparisonOperator (enum)
• OnlineEvaluator.CloudObservability.OpenTelemetry (message)
• OnlineEvaluator.CloudObservability.TraceScope (message)
• OnlineEvaluator.CloudObservability.TraceScope.Predicate (message)
• OnlineEvaluator.Config (message)
• OnlineEvaluator.Config.RandomSampling (message)
• OnlineEvaluator.State (enum)
• OnlineEvaluator.StateDetails (message)
• OutputConfig (message)
• OutputFieldSpec (message)
• OutputFieldSpec.FieldType (enum)
• OutputInfo (message)
• PSCAutomationConfig (message)
• PSCAutomationState (enum)
• PairwiseChoice (enum)
• PairwiseMetricInput (message)
• PairwiseMetricInstance (message)
• PairwiseMetricResult (message)
• PairwiseMetricSpec (message)
• PairwiseQuestionAnsweringQualityInput (message)
• PairwiseQuestionAnsweringQualityInstance (message)
• PairwiseQuestionAnsweringQualityResult (message)
• PairwiseQuestionAnsweringQualitySpec (message)
• PairwiseSummarizationQualityInput (message)
• PairwiseSummarizationQualityInstance (message)
• PairwiseSummarizationQualityResult (message)
• PairwiseSummarizationQualitySpec (message)
• Part (message)
• Part.MediaResolution (message)
• Part.MediaResolution.Level (enum)
• PartialArg (message)
• PartnerModelTuningSpec (message)
• PauseModelDeploymentMonitoringJobRequest (message)
• PauseScheduleRequest (message)
• PersistentDiskSpec (message)
• PersistentResource (message)
• PersistentResource.State (enum)
• PipelineFailurePolicy (enum)
• PipelineJob (message)
• PipelineJob.RuntimeConfig (message)
• PipelineJob.RuntimeConfig.DefaultRuntime (message)
• PipelineJob.RuntimeConfig.InputArtifact (message)
• PipelineJob.RuntimeConfig.PersistentResourceRuntimeDetail (message)
• PipelineJob.RuntimeConfig.PersistentResourceRuntimeDetail.TaskResourceUnavailableTimeoutBehavior (enum)
• PipelineJobDetail (message)
• PipelineState (enum)
• PipelineTaskDetail (message)
• PipelineTaskDetail.ArtifactList (message)
• PipelineTaskDetail.PipelineTaskStatus (message)
• PipelineTaskDetail.State (enum)
• PipelineTaskExecutorDetail (message)
• PipelineTaskExecutorDetail.ContainerDetail (message)
• PipelineTaskExecutorDetail.CustomJobDetail (message)
• PipelineTaskRerunConfig (message)
• PipelineTaskRerunConfig.ArtifactList (message)
• PipelineTaskRerunConfig.Inputs (message)
• PipelineTemplateMetadata (message)
• PointwiseMetricInput (message)
• PointwiseMetricInstance (message)
• PointwiseMetricResult (message)
• PointwiseMetricSpec (message)
• Port (message)
• PostStartupScriptConfig (message)
• PostStartupScriptConfig.PostStartupScriptBehavior (enum)
• PreTunedModel (message)
• PrebuiltVoiceConfig (message)
• PredefinedMetricSpec (message)
• PredefinedSplit (message)
• PredictLongRunningMetadata (message)
• PredictLongRunningRequest (message)
• PredictLongRunningResponse (message)
• PredictRequest (message)
• PredictRequestResponseLoggingConfig (message)
• PredictResponse (message)
• PredictSchemata (message)
• Presets (message)
• Presets.Modality (enum)
• Presets.Query (enum)
• PrivateEndpoints (message)
• PrivateServiceConnectConfig (message)
• Probe (message)
• Probe.ExecAction (message)
• Probe.GrpcAction (message)
• Probe.HttpGetAction (message)
• Probe.HttpHeader (message)
• Probe.TcpSocketAction (message)
• PscAutomatedEndpoints (message)
• PscInterfaceConfig (message)
• PublisherModel (message)
• PublisherModel.CallToAction (message)
• PublisherModel.CallToAction.Deploy (message)
• PublisherModel.CallToAction.Deploy.DeployMetadata (message)
• PublisherModel.CallToAction.DeployGke (message)
• PublisherModel.CallToAction.DeployVertex (message)
• PublisherModel.CallToAction.OpenFineTuningPipelines (message)
• PublisherModel.CallToAction.OpenNotebooks (message)
• PublisherModel.CallToAction.RegionalResourceReferences (message)
• PublisherModel.CallToAction.ViewRestApi (message)
• PublisherModel.Documentation (message)
• PublisherModel.LaunchStage (enum)
• PublisherModel.OpenSourceCategory (enum)
• PublisherModel.Parent (message)
• PublisherModel.ResourceReference (message)
• PublisherModel.VersionState (enum)
• PublisherModelConfig (message)
• PublisherModelEulaAcceptance (message)
• PublisherModelView (enum)
• PurgeArtifactsMetadata (message)
• PurgeArtifactsRequest (message)
• PurgeArtifactsResponse (message)
• PurgeContextsMetadata (message)
• PurgeContextsRequest (message)
• PurgeContextsResponse (message)
• PurgeExecutionsMetadata (message)
• PurgeExecutionsRequest (message)
• PurgeExecutionsResponse (message)
• PythonPackageSpec (message)
• QueryArtifactLineageSubgraphRequest (message)
• QueryContextLineageSubgraphRequest (message)
• QueryDeployedModelsRequest (message)
• QueryDeployedModelsResponse (message)
• QueryExecutionInputsAndOutputsRequest (message)
• QueryExtensionRequest (message)
• QueryExtensionResponse (message)
• QueryReasoningEngineRequest (message)
• QueryReasoningEngineResponse (message)
• QuestionAnsweringCorrectnessInput (message)
• QuestionAnsweringCorrectnessInstance (message)
• QuestionAnsweringCorrectnessResult (message)
• QuestionAnsweringCorrectnessSpec (message)
• QuestionAnsweringHelpfulnessInput (message)
• QuestionAnsweringHelpfulnessInstance (message)
• QuestionAnsweringHelpfulnessResult (message)
• QuestionAnsweringHelpfulnessSpec (message)
• QuestionAnsweringQualityInput (message)
• QuestionAnsweringQualityInstance (message)
• QuestionAnsweringQualityResult (message)
• QuestionAnsweringQualitySpec (message)
• QuestionAnsweringRelevanceInput (message)
• QuestionAnsweringRelevanceInstance (message)
• QuestionAnsweringRelevanceResult (message)
• QuestionAnsweringRelevanceSpec (message)
• RagChunk (message)
• RagChunk.PageSpan (message)
• RagContexts (message)
• RagContexts.Context (message)
• RagCorpus (message)
• RagCorpus.CorpusTypeConfig (message)
• RagCorpus.CorpusTypeConfig.DocumentCorpus (message)
• RagCorpus.CorpusTypeConfig.MemoryCorpus (message)
• RagDataSchema (message)
• RagEmbeddingModelConfig (message)
• RagEmbeddingModelConfig.HybridSearchConfig (message)
• RagEmbeddingModelConfig.SparseEmbeddingConfig (message)
• RagEmbeddingModelConfig.SparseEmbeddingConfig.Bm25 (message)
• RagEmbeddingModelConfig.VertexPredictionEndpoint (message)
• RagEngineConfig (message)
• RagFile (message)
• RagFile.RagFileType (enum)
• RagFileChunkingConfig (message)
• RagFileChunkingConfig.FixedLengthChunking (message)
• RagFileMetadataConfig (message)
• RagFileParsingConfig (message)
• RagFileParsingConfig.AdvancedParser (message)
• RagFileParsingConfig.LayoutParser (message)
• RagFileParsingConfig.LlmParser (message)
• RagFileTransformationConfig (message)
• RagManagedDbConfig (message)
• RagManagedDbConfig.Basic (message)
• RagManagedDbConfig.Enterprise (message) (deprecated)
• RagManagedDbConfig.Scaled (message)
• RagManagedDbConfig.Serverless (message)
• RagManagedDbConfig.Spanner (message)
• RagManagedDbConfig.Unprovisioned (message)
• RagMetadata (message)
• RagMetadataSchemaDetails (message)
• RagMetadataSchemaDetails.DataType (enum)
• RagMetadataSchemaDetails.Granularity (enum)
• RagMetadataSchemaDetails.ListConfig (message)
• RagMetadataSchemaDetails.SearchStrategy (message)
• RagMetadataSchemaDetails.SearchStrategy.SearchStrategyType (enum)
• RagQuery (message)
• RagQuery.Ranking (message)
• RagRetrievalConfig (message)
• RagRetrievalConfig.Filter (message)
• RagRetrievalConfig.HybridSearch (message)
• RagRetrievalConfig.Ranking (message)
• RagRetrievalConfig.Ranking.LlmRanker (message)
• RagRetrievalConfig.Ranking.RankService (message)
• RagVectorDbConfig (message)
• RagVectorDbConfig.Pinecone (message)
• RagVectorDbConfig.RagManagedDb (message)
• RagVectorDbConfig.RagManagedDb.ANN (message)
• RagVectorDbConfig.RagManagedDb.KNN (message)
• RagVectorDbConfig.RagManagedVertexVectorSearch (message)
• RagVectorDbConfig.VertexFeatureStore (message)
• RagVectorDbConfig.VertexVectorSearch (message)
• RagVectorDbConfig.Weaviate (message)
• RawOutput (message)
• RawPredictRequest (message)
• RayLogsSpec (message)
• RayMetricSpec (message)
• RaySpec (message)
• ReadFeatureValuesRequest (message)
• ReadFeatureValuesResponse (message)
• ReadFeatureValuesResponse.EntityView (message)
• ReadFeatureValuesResponse.EntityView.Data (message)
• ReadFeatureValuesResponse.FeatureDescriptor (message)
• ReadFeatureValuesResponse.Header (message)
• ReadTensorboardBlobDataRequest (message)
• ReadTensorboardBlobDataResponse (message)
• ReadTensorboardSizeRequest (message)
• ReadTensorboardSizeResponse (message)
• ReadTensorboardTimeSeriesDataRequest (message)
• ReadTensorboardTimeSeriesDataResponse (message)
• ReadTensorboardUsageRequest (message)
• ReadTensorboardUsageResponse (message)
• ReadTensorboardUsageResponse.PerMonthUsageData (message)
• ReadTensorboardUsageResponse.PerUserUsageData (message)
• ReasoningEngine (message)
• ReasoningEngine.TrafficConfig (message)
• ReasoningEngine.TrafficConfig.TrafficSplitAlwaysLatest (message)
• ReasoningEngine.TrafficConfig.TrafficSplitManual (message)
• ReasoningEngine.TrafficConfig.TrafficSplitManual.Target (message)
• ReasoningEngineContextSpec (message)
• ReasoningEngineContextSpec.MemoryBankConfig (message)
• ReasoningEngineContextSpec.MemoryBankConfig.GenerationConfig (message)
• ReasoningEngineContextSpec.MemoryBankConfig.SimilaritySearchConfig (message)
• ReasoningEngineContextSpec.MemoryBankConfig.TtlConfig (message)
• ReasoningEngineContextSpec.MemoryBankConfig.TtlConfig.GranularTtlConfig (message)
• ReasoningEngineRuntimeRevision (message)
• ReasoningEngineRuntimeRevision.State (enum)
• ReasoningEngineSpec (message)
• ReasoningEngineSpec.ContainerSpec (message)
• ReasoningEngineSpec.DeploymentSpec (message)
• ReasoningEngineSpec.DeploymentSpec.AgentGatewayConfig (message)
• ReasoningEngineSpec.DeploymentSpec.AgentGatewayConfig.AgentToAnywhereConfig (message)
• ReasoningEngineSpec.DeploymentSpec.AgentGatewayConfig.ClientToAgentConfig (message)
• ReasoningEngineSpec.IdentityType (enum)
• ReasoningEngineSpec.PackageSpec (message)
• ReasoningEngineSpec.SourceCodeSpec (message)
• ReasoningEngineSpec.SourceCodeSpec.AgentConfigSource (message)
• ReasoningEngineSpec.SourceCodeSpec.AgentConfigSource.AdkConfig (message)
• ReasoningEngineSpec.SourceCodeSpec.DeveloperConnectConfig (message)
• ReasoningEngineSpec.SourceCodeSpec.DeveloperConnectSource (message)
• ReasoningEngineSpec.SourceCodeSpec.ImageSpec (message)
• ReasoningEngineSpec.SourceCodeSpec.InlineSource (message)
• ReasoningEngineSpec.SourceCodeSpec.PythonSpec (message)
• RebaseTunedModelOperationMetadata (message)
• RebaseTunedModelRequest (message)
• RebootPersistentResourceOperationMetadata (message)
• RebootPersistentResourceRequest (message)
• RecommendSpecRequest (message)
• RecommendSpecResponse (message)
• RecommendSpecResponse.MachineAndModelContainerSpec (message)
• RecommendSpecResponse.Recommendation (message)
• RecommendSpecResponse.Recommendation.QuotaState (enum)
• RemoveContextChildrenRequest (message)
• RemoveContextChildrenResponse (message)
• RemoveDatapointsRequest (message)
• RemoveDatapointsResponse (message)
• RemoveExamplesRequest (message)
• RemoveExamplesResponse (message)
• ReplicatedVoiceConfig (message)
• ReservationAffinity (message)
• ReservationAffinity.Type (enum)
• ResourcePool (message)
• ResourcePool.AutoscalingSpec (message)
• ResourceRuntime (message)
• ResourceRuntimeSpec (message)
• ResourcesConsumed (message)
• RestoreDatasetVersionOperationMetadata (message)
• RestoreDatasetVersionRequest (message)
• ResumeModelDeploymentMonitoringJobRequest (message)
• ResumeScheduleRequest (message)
• Retrieval (message)
• RetrievalConfig (message)
• RetrievalMetadata (message)
• RetrieveContextsRequest (message)
• RetrieveContextsRequest.VertexRagStore (message)
• RetrieveContextsRequest.VertexRagStore.RagResource (message)
• RetrieveContextsResponse (message)
• RetrieveMemoriesRequest (message)
• RetrieveMemoriesRequest.SimilaritySearchParams (message)
• RetrieveMemoriesRequest.SimpleRetrievalParams (message)
• RetrieveMemoriesResponse (message)
• RetrieveMemoriesResponse.RetrievedMemory (message)
• RetrieveProfilesRequest (message)
• RetrieveProfilesResponse (message)
• RetrieveSkillsRequest (message)
• RetrieveSkillsResponse (message)
• RetrieveSkillsResponse.RetrievedSkill (message)
• RolloutOptions (message)
• RougeInput (message)
• RougeInstance (message)
• RougeMetricValue (message)
• RougeResults (message)
• RougeSpec (message)
• Rubric (message)
• Rubric.Content (message)
• Rubric.Content.Property (message)
• Rubric.Importance (enum)
• RubricBasedInstructionFollowingInput (message)
• RubricBasedInstructionFollowingInstance (message)
• RubricBasedInstructionFollowingResult (message)
• RubricBasedInstructionFollowingSpec (message)
• RubricCritiqueResult (message)
• RubricGenerationSpec (message)
• RubricGenerationSpec.RubricContentType (enum)
• RubricGroup (message)
• RubricVerdict (message)
• RuntimeArtifact (message)
• RuntimeConfig (message)
• RuntimeConfig.CodeInterpreterRuntimeConfig (message)
• RuntimeConfig.VertexAISearchRuntimeConfig (message)
• SafetyInput (message)
• SafetyInstance (message)
• SafetyRating (message)
• SafetyRating.HarmProbability (enum)
• SafetyRating.HarmSeverity (enum)
• SafetyResult (message)
• SafetySetting (message)
• SafetySetting.HarmBlockMethod (enum)
• SafetySetting.HarmBlockThreshold (enum)
• SafetySpec (message)
• SampledShapleyAttribution (message)
• SamplingStrategy (message)
• SamplingStrategy.RandomSampleConfig (message)
• SavedQuery (message)
• Scalar (message)
• Schedule (message)
• Schedule.RunResponse (message)
• Schedule.State (enum)
• ScheduleConfig (message)
• Scheduling (message)
• Scheduling.Strategy (enum)
• Schema (message)
• SearchDataItemsRequest (message)
• SearchDataItemsRequest.OrderByAnnotation (message)
• SearchDataItemsResponse (message)
• SearchEntryPoint (message)
• SearchExamplesRequest (message)
• SearchExamplesResponse (message)
• SearchExamplesResponse.SimilarExample (message)
• SearchFeaturesRequest (message)
• SearchFeaturesResponse (message)
• SearchMigratableResourcesRequest (message)
• SearchMigratableResourcesResponse (message)
• SearchModelDeploymentMonitoringStatsAnomaliesRequest (message)
• SearchModelDeploymentMonitoringStatsAnomaliesRequest.StatsAnomaliesObjective (message)
• SearchModelDeploymentMonitoringStatsAnomaliesResponse (message)
• SearchModelMonitoringAlertsRequest (message)
• SearchModelMonitoringAlertsResponse (message)
• SearchModelMonitoringStatsFilter (message)
• SearchModelMonitoringStatsFilter.TabularStatsFilter (message)
• SearchModelMonitoringStatsRequest (message)
• SearchModelMonitoringStatsResponse (message)
• SearchNearestEntitiesRequest (message)
• SearchNearestEntitiesResponse (message)
• SecretEnvVar (message)
• SecretRef (message)
• Segment (message)
• ServiceAccountSpec (message)
• Session (message)
• SessionEvent (message)
• SetPublisherModelConfigOperationMetadata (message)
• SetPublisherModelConfigRequest (message)
• SharePointSources (message)
• SharePointSources.SharePointSource (message)
• ShieldedVmConfig (message)
• Skill (message)
• Skill.SkillSource (enum)
• Skill.State (enum)
• SkillRevision (message)
• SkillRevision.State (enum)
• SlackSource (message)
• SlackSource.SlackChannels (message)
• SlackSource.SlackChannels.SlackChannel (message)
• SmoothGradConfig (message)
• SpeakerVoiceConfig (message)
• SpecialistPool (message)
• SpeculativeDecodingSpec (message)
• SpeculativeDecodingSpec.DraftModelSpeculation (message)
• SpeculativeDecodingSpec.NgramSpeculation (message)
• SpeechConfig (message)
• StartNotebookRuntimeOperationMetadata (message)
• StartNotebookRuntimeRequest (message)
• StartNotebookRuntimeResponse (message)
• StopNotebookRuntimeOperationMetadata (message)
• StopNotebookRuntimeRequest (message)
• StopNotebookRuntimeResponse (message)
• StopTrialRequest (message)
• StoredContentsExample (message)
• StoredContentsExample.SearchKeyGenerationMethod (message)
• StoredContentsExample.SearchKeyGenerationMethod.LastEntry (message)
• StoredContentsExampleFilter (message)
• StoredContentsExampleParameters (message)
• StoredContentsExampleParameters.ContentSearchKey (message)
• StratifiedSplit (message)
• StreamDirectPredictRequest (message)
• StreamDirectPredictResponse (message)
• StreamDirectRawPredictRequest (message)
• StreamDirectRawPredictResponse (message)
• StreamQueryReasoningEngineRequest (message)
• StreamRawPredictRequest (message)
• StreamingFetchFeatureValuesRequest (message)
• StreamingFetchFeatureValuesResponse (message)
• StreamingPredictRequest (message)
• StreamingPredictResponse (message)
• StreamingRawPredictRequest (message)
• StreamingRawPredictResponse (message)
• StreamingReadFeatureValuesRequest (message)
• StringArray (message)
• StructFieldValue (message)
• StructValue (message)
• StructuredMemoryConfig (message)
• StructuredMemoryConfig.SchemaConfig (message)
• Study (message)
• Study.State (enum)
• StudySpec (message)
• StudySpec.Algorithm (enum)
• StudySpec.ConvexAutomatedStoppingSpec (message)
• StudySpec.ConvexStopConfig (message) (deprecated)
• StudySpec.DecayCurveAutomatedStoppingSpec (message)
• StudySpec.MeasurementSelectionType (enum)
• StudySpec.MedianAutomatedStoppingSpec (message)
• StudySpec.MetricSpec (message)
• StudySpec.MetricSpec.GoalType (enum)
• StudySpec.MetricSpec.SafetyMetricConfig (message)
• StudySpec.ObservationNoise (enum)
• StudySpec.ParameterSpec (message)
• StudySpec.ParameterSpec.CategoricalValueSpec (message)
• StudySpec.ParameterSpec.ConditionalParameterSpec (message)
• StudySpec.ParameterSpec.ConditionalParameterSpec.CategoricalValueCondition (message)
• StudySpec.ParameterSpec.ConditionalParameterSpec.DiscreteValueCondition (message)
• StudySpec.ParameterSpec.ConditionalParameterSpec.IntValueCondition (message)
• StudySpec.ParameterSpec.DiscreteValueSpec (message)
• StudySpec.ParameterSpec.DoubleValueSpec (message)
• StudySpec.ParameterSpec.IntegerValueSpec (message)
• StudySpec.ParameterSpec.ScaleType (enum)
• StudySpec.StudyStoppingConfig (message)
• StudySpec.TransferLearningConfig (message)
• StudyTimeConstraint (message)
• SuggestTrialsMetadata (message)
• SuggestTrialsRequest (message)
• SuggestTrialsResponse (message)
• SummarizationHelpfulnessInput (message)
• SummarizationHelpfulnessInstance (message)
• SummarizationHelpfulnessResult (message)
• SummarizationHelpfulnessSpec (message)
• SummarizationQualityInput (message)
• SummarizationQualityInstance (message)
• SummarizationQualityResult (message)
• SummarizationQualitySpec (message)
• SummarizationVerbosityInput (message)
• SummarizationVerbosityInstance (message)
• SummarizationVerbosityResult (message)
• SummarizationVerbositySpec (message)
• SupervisedHyperParameters (message)
• SupervisedHyperParameters.AdapterSize (enum)
• SupervisedTuningDataStats (message)
• SupervisedTuningDatasetDistribution (message)
• SupervisedTuningDatasetDistribution.DatasetBucket (message)
• SupervisedTuningSpec (message)
• SupervisedTuningSpec.TuningMode (enum)
• SuspendOnlineEvaluatorOperationMetadata (message)
• SuspendOnlineEvaluatorRequest (message)
• SyncFeatureViewRequest (message)
• SyncFeatureViewResponse (message)
• SyntheticExample (message)
• SyntheticField (message)
• TFRecordDestination (message)
• TaskDescriptionStrategy (message)
• Tensor (message)
• Tensor.DataType (enum)
• Tensorboard (message)
• TensorboardBlob (message)
• TensorboardBlobSequence (message)
• TensorboardExperiment (message)
• TensorboardRun (message)
• TensorboardTensor (message)
• TensorboardTimeSeries (message)
• TensorboardTimeSeries.Metadata (message)
• TensorboardTimeSeries.ValueType (enum)
• ThresholdConfig (message)
• TimeSeriesData (message)
• TimeSeriesDataPoint (message)
• TimestampSplit (message)
• TokensInfo (message)
• Tool (message)
• Tool.CodeExecution (message)
• Tool.ComputerUse (message)
• Tool.ComputerUse.Environment (enum)
• Tool.GoogleSearch (message)
• Tool.ParallelAiSearch (message)
• Tool.PhishBlockThreshold (enum)
• ToolCall (message)
• ToolCallValidInput (message)
• ToolCallValidInstance (message)
• ToolCallValidMetricValue (message)
• ToolCallValidResults (message)
• ToolCallValidSpec (message)
• ToolConfig (message)
• ToolNameMatchInput (message)
• ToolNameMatchInstance (message)
• ToolNameMatchMetricValue (message)
• ToolNameMatchResults (message)
• ToolNameMatchSpec (message)
• ToolParameterKVMatchInput (message)
• ToolParameterKVMatchInstance (message)
• ToolParameterKVMatchMetricValue (message)
• ToolParameterKVMatchResults (message)
• ToolParameterKVMatchSpec (message)
• ToolParameterKeyMatchInput (message)
• ToolParameterKeyMatchInstance (message)
• ToolParameterKeyMatchMetricValue (message)
• ToolParameterKeyMatchResults (message)
• ToolParameterKeyMatchSpec (message)
• ToolUseExample (message)
• ToolUseExample.ExtensionOperation (message)
• TrainingPipeline (message)
• Trajectory (message)
• TrajectoryAnyOrderMatchInput (message)
• TrajectoryAnyOrderMatchInstance (message)
• TrajectoryAnyOrderMatchMetricValue (message)
• TrajectoryAnyOrderMatchResults (message)
• TrajectoryAnyOrderMatchSpec (message)
• TrajectoryExactMatchInput (message)
• TrajectoryExactMatchInstance (message)
• TrajectoryExactMatchMetricValue (message)
• TrajectoryExactMatchResults (message)
• TrajectoryExactMatchSpec (message)
• TrajectoryInOrderMatchInput (message)
• TrajectoryInOrderMatchInstance (message)
• TrajectoryInOrderMatchMetricValue (message)
• TrajectoryInOrderMatchResults (message)
• TrajectoryInOrderMatchSpec (message)
• TrajectoryPrecisionInput (message)
• TrajectoryPrecisionInstance (message)
• TrajectoryPrecisionMetricValue (message)
• TrajectoryPrecisionResults (message)
• TrajectoryPrecisionSpec (message)
• TrajectoryRecallInput (message)
• TrajectoryRecallInstance (message)
• TrajectoryRecallMetricValue (message)
• TrajectoryRecallResults (message)
• TrajectoryRecallSpec (message)
• TrajectorySingleToolUseInput (message)
• TrajectorySingleToolUseInstance (message)
• TrajectorySingleToolUseMetricValue (message)
• TrajectorySingleToolUseResults (message)
• TrajectorySingleToolUseSpec (message)
• Transcription (message)
• Trial (message)
• Trial.Parameter (message)
• Trial.State (enum)
• TrialContext (message)
• TunedModel (message)
• TunedModelCheckpoint (message)
• TunedModelRef (message)
• TuningDataStats (message)
• TuningJob (message)
• Type (enum)
• UndeployIndexOperationMetadata (message)
• UndeployIndexRequest (message)
• UndeployIndexResponse (message)
• UndeployModelOperationMetadata (message)
• UndeployModelRequest (message)
• UndeployModelResponse (message)
• UndeploySolverOperationMetadata (message)
• UnmanagedContainerModel (message)
• UpdateAgentRequest (message)
• UpdateArtifactRequest (message)
• UpdateCachedContentRequest (message)
• UpdateContextRequest (message)
• UpdateDatasetRequest (message)
• UpdateDatasetVersionRequest (message)
• UpdateDeploymentResourcePoolOperationMetadata (message)
• UpdateDeploymentResourcePoolRequest (message)
• UpdateEndpointLongRunningRequest (message)
• UpdateEndpointOperationMetadata (message)
• UpdateEndpointRequest (message)
• UpdateEntityTypeRequest (message)
• UpdateExampleStoreOperationMetadata (message)
• UpdateExampleStoreRequest (message)
• UpdateExecutionRequest (message)
• UpdateExplanationDatasetOperationMetadata (message)
• UpdateExplanationDatasetRequest (message)
• UpdateExplanationDatasetResponse (message)
• UpdateExtensionRequest (message)
• UpdateFeatureGroupOperationMetadata (message)
• UpdateFeatureGroupRequest (message)
• UpdateFeatureMonitorOperationMetadata (message)
• UpdateFeatureMonitorRequest (message)
• UpdateFeatureOnlineStoreOperationMetadata (message)
• UpdateFeatureOnlineStoreRequest (message)
• UpdateFeatureOperationMetadata (message)
• UpdateFeatureRequest (message)
• UpdateFeatureViewOperationMetadata (message)
• UpdateFeatureViewRequest (message)
• UpdateFeaturestoreOperationMetadata (message)
• UpdateFeaturestoreRequest (message)
• UpdateIndexEndpointRequest (message)
• UpdateIndexOperationMetadata (message)
• UpdateIndexRequest (message)
• UpdateMemoryOperationMetadata (message)
• UpdateMemoryRequest (message)
• UpdateModelDeploymentMonitoringJobOperationMetadata (message)
• UpdateModelDeploymentMonitoringJobRequest (message)
• UpdateModelMonitorOperationMetadata (message)
• UpdateModelMonitorRequest (message)
• UpdateModelRequest (message)
• UpdateNotebookRuntimeTemplateRequest (message)
• UpdateOnlineEvaluatorOperationMetadata (message)
• UpdateOnlineEvaluatorRequest (message)
• UpdatePersistentResourceOperationMetadata (message)
• UpdatePersistentResourceRequest (message)
• UpdateRagCorpusOperationMetadata (message)
• UpdateRagCorpusRequest (message)
• UpdateRagEngineConfigOperationMetadata (message)
• UpdateRagEngineConfigRequest (message)
• UpdateRagMetadataRequest (message)
• UpdateReasoningEngineOperationMetadata (message)
• UpdateReasoningEngineRequest (message)
• UpdateScheduleRequest (message)
• UpdateSessionRequest (message)
• UpdateSkillOperationMetadata (message)
• UpdateSkillRequest (message)
• UpdateSpecialistPoolOperationMetadata (message)
• UpdateSpecialistPoolRequest (message)
• UpdateTensorboardExperimentRequest (message)
• UpdateTensorboardOperationMetadata (message)
• UpdateTensorboardRequest (message)
• UpdateTensorboardRunRequest (message)
• UpdateTensorboardTimeSeriesRequest (message)
• UpgradeNotebookRuntimeOperationMetadata (message)
• UpgradeNotebookRuntimeRequest (message)
• UpgradeNotebookRuntimeResponse (message)
• UploadModelOperationMetadata (message)
• UploadModelRequest (message)
• UploadModelResponse (message)
• UploadRagFileConfig (message)
• UpsertDatapointsRequest (message)
• UpsertDatapointsResponse (message)
• UpsertExamplesRequest (message)
• UpsertExamplesResponse (message)
• UpsertExamplesResponse.UpsertResult (message)
• UrlContext (message)
• UrlContextMetadata (message)
• UrlMetadata (message)
• UrlMetadata.UrlRetrievalStatus (enum)
• UsageMetadata (message)
• UsageMetadata.TrafficType (enum)
• UserActionReference (message)
• UserSpecifiedMetadata (message)
• Value (message)
• VeoHyperParameters (message)
• VeoHyperParameters.AdapterSize (enum)
• VeoHyperParameters.TuningSpeed (enum)
• VeoHyperParameters.TuningTask (enum)
• VeoLoraTuningSpec (message)
• VeoTuningSpec (message)
• VertexAISearch (message)
• VertexAISearch.DataStoreSpec (message)
• VertexAiSearchConfig (message)
• VertexMultimodalDatasetDestination (message)
• VertexMultimodalDatasetSource (message)
• VertexRagStore (message)
• VertexRagStore.RagResource (message)
• VideoMetadata (message)
• VoiceConfig (message)
• WorkerPoolSpec (message)
• WriteFeatureValuesPayload (message)
• WriteFeatureValuesRequest (message)
• WriteFeatureValuesResponse (message)
• WriteTensorboardExperimentDataRequest (message)
• WriteTensorboardExperimentDataResponse (message)
• WriteTensorboardRunDataRequest (message)
• WriteTensorboardRunDataResponse (message)
• XraiAttribution (message)

AgentService

The service that manages Vertex Agent related resources.

CreateAgent
rpc CreateAgent(CreateAgentRequest) returns (Operation) Creates an agent. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.agents.create For more information, see the IAM documentation.

DeleteAgent
rpc DeleteAgent(DeleteAgentRequest) returns (Operation) Deletes an agent. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.agents.delete For more information, see the IAM documentation.

GetAgent
rpc GetAgent(GetAgentRequest) returns (Agent) Retrieves an agent. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.agents.get For more information, see the IAM documentation.

ListAgents
rpc ListAgents(ListAgentsRequest) returns (ListAgentsResponse) Lists agents in a location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.agents.list For more information, see the IAM documentation.

UpdateAgent
rpc UpdateAgent(UpdateAgentRequest) returns (Agent) Updates an agent. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.agents.update For more information, see the IAM documentation.

DataFoundryService

Service for generating and preparing datasets for Gen AI evaluation.

GenerateSyntheticData
rpc GenerateSyntheticData(GenerateSyntheticDataRequest) returns (GenerateSyntheticDataResponse) Generates synthetic (artificial) data based on a description Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

DatasetService

The service that manages Agent Platform Dataset and its child resources.

AssembleData
rpc AssembleData(AssembleDataRequest) returns (Operation) Assembles each row of a multimodal dataset and writes the result into a BigQuery table. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

AssessData
rpc AssessData(AssessDataRequest) returns (Operation) Assesses the state or validity of the dataset with respect to a given use case. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

CreateDataset
rpc CreateDataset(CreateDatasetRequest) returns (Operation) Creates a Dataset. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.datasets.create For more information, see the IAM documentation.

CreateDatasetVersion

rpc CreateDatasetVersion(CreateDatasetVersionRequest) returns (Operation) Create a version from a Dataset. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.datasetVersions.create For more information, see the IAM documentation.

DeleteDataset
rpc DeleteDataset(DeleteDatasetRequest) returns (Operation) Deletes a Dataset. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.datasets.delete For more information, see the IAM documentation.

DeleteDatasetVersion

rpc DeleteDatasetVersion(DeleteDatasetVersionRequest) returns (Operation) Deletes a Dataset version. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.datasetVersions.delete For more information, see the IAM documentation.

DeleteSavedQuery
rpc DeleteSavedQuery(DeleteSavedQueryRequest) returns (Operation) Deletes a SavedQuery. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.datasets.delete For more information, see the IAM documentation.

ExportData
rpc ExportData(ExportDataRequest) returns (Operation) Exports data from a Dataset. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.datasets.export For more information, see the IAM documentation.

GetAnnotationSpec

rpc GetAnnotationSpec(GetAnnotationSpecRequest) returns (AnnotationSpec) Gets an AnnotationSpec. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.annotationSpecs.get For more information, see the IAM documentation.

GetDataset
rpc GetDataset(GetDatasetRequest) returns (Dataset) Gets a Dataset. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.datasets.get For more information, see the IAM documentation.

GetDatasetVersion

rpc GetDatasetVersion(GetDatasetVersionRequest) returns (DatasetVersion) Gets a Dataset version. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.datasetVersions.get For more information, see the IAM documentation.

ImportData
rpc ImportData(ImportDataRequest) returns (Operation) Imports data into a Dataset. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.datasets.import For more information, see the IAM documentation.

ListAnnotations

rpc ListAnnotations(ListAnnotationsRequest) returns (ListAnnotationsResponse) Lists Annotations belongs to a dataitem. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.annotations.list For more information, see the IAM documentation.

ListDataItems

rpc ListDataItems(ListDataItemsRequest) returns (ListDataItemsResponse) Lists DataItems in a Dataset. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.dataItems.list For more information, see the IAM documentation.

ListDatasetVersions

rpc ListDatasetVersions(ListDatasetVersionsRequest) returns (ListDatasetVersionsResponse) Lists DatasetVersions in a Dataset. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.datasetVersions.list For more information, see the IAM documentation.

ListDatasets

rpc ListDatasets(ListDatasetsRequest) returns (ListDatasetsResponse) Lists Datasets in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.datasets.list For more information, see the IAM documentation.

ListSavedQueries

rpc ListSavedQueries(ListSavedQueriesRequest) returns (ListSavedQueriesResponse) Lists SavedQueries in a Dataset. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.datasets.get For more information, see the IAM documentation.

RestoreDatasetVersion

rpc RestoreDatasetVersion(RestoreDatasetVersionRequest) returns (Operation) Restores a dataset version. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.datasetVersions.restore For more information, see the IAM documentation.

SearchDataItems

rpc SearchDataItems(SearchDataItemsRequest) returns (SearchDataItemsResponse) Searches DataItems in a Dataset. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the dataset resource: aiplatform.dataItems.list For more information, see the IAM documentation.

UpdateDataset
rpc UpdateDataset(UpdateDatasetRequest) returns (Dataset) Updates a Dataset. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.datasets.update For more information, see the IAM documentation.

UpdateDatasetVersion
rpc UpdateDatasetVersion(UpdateDatasetVersionRequest) returns (DatasetVersion) Updates a DatasetVersion. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

DeploymentResourcePoolService

A service that manages the DeploymentResourcePool resource.

CreateDeploymentResourcePool

rpc CreateDeploymentResourcePool(CreateDeploymentResourcePoolRequest) returns (Operation) Create a DeploymentResourcePool. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.deploymentResourcePools.create For more information, see the IAM documentation.

DeleteDeploymentResourcePool

rpc DeleteDeploymentResourcePool(DeleteDeploymentResourcePoolRequest) returns (Operation) Delete a DeploymentResourcePool. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.deploymentResourcePools.delete For more information, see the IAM documentation.

GetDeploymentResourcePool

rpc GetDeploymentResourcePool(GetDeploymentResourcePoolRequest) returns (DeploymentResourcePool) Get a DeploymentResourcePool. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.deploymentResourcePools.get For more information, see the IAM documentation.

ListDeploymentResourcePools

rpc ListDeploymentResourcePools(ListDeploymentResourcePoolsRequest) returns (ListDeploymentResourcePoolsResponse) List DeploymentResourcePools in a location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.deploymentResourcePools.list For more information, see the IAM documentation.

QueryDeployedModels

rpc QueryDeployedModels(QueryDeployedModelsRequest) returns (QueryDeployedModelsResponse) List DeployedModels that have been deployed on this DeploymentResourcePool. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the deploymentResourcePool resource: aiplatform.deploymentResourcePools.queryDeployedModels For more information, see the IAM documentation.

UpdateDeploymentResourcePool

rpc UpdateDeploymentResourcePool(UpdateDeploymentResourcePoolRequest) returns (Operation) Update a DeploymentResourcePool. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.deploymentResourcePools.update For more information, see the IAM documentation.

EndpointService

A service for managing Agent Platform's Endpoints.

CreateEndpoint
rpc CreateEndpoint(CreateEndpointRequest) returns (Operation) Creates an Endpoint. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.endpoints.create For more information, see the IAM documentation.

DeleteEndpoint
rpc DeleteEndpoint(DeleteEndpointRequest) returns (Operation) Deletes an Endpoint. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.endpoints.delete For more information, see the IAM documentation.

DeployModel

rpc DeployModel(DeployModelRequest) returns (Operation) Deploys a Model into this Endpoint, creating a DeployedModel within it. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the endpoint resource: aiplatform.endpoints.deploy For more information, see the IAM documentation.

FetchPublisherModelConfig
rpc FetchPublisherModelConfig(FetchPublisherModelConfigRequest) returns (PublisherModelConfig) Fetches the configs of publisher models. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

GetEndpoint
rpc GetEndpoint(GetEndpointRequest) returns (Endpoint) Gets an Endpoint. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.endpoints.get For more information, see the IAM documentation.

ListEndpoints

rpc ListEndpoints(ListEndpointsRequest) returns (ListEndpointsResponse) Lists Endpoints in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.endpoints.list For more information, see the IAM documentation.

MutateDeployedModel

rpc MutateDeployedModel(MutateDeployedModelRequest) returns (Operation) Updates an existing deployed model. Updatable fields include min_replica_count, max_replica_count, required_replica_count, autoscaling_metric_specs, disable_container_logging (v1 only), and enable_container_logging (v1beta1 only). Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the endpoint resource: aiplatform.endpoints.deploy For more information, see the IAM documentation.

SetPublisherModelConfig
rpc SetPublisherModelConfig(SetPublisherModelConfigRequest) returns (Operation) Sets (creates or updates) configs of publisher models. For example, sets the request/response logging config. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

UndeployModel

rpc UndeployModel(UndeployModelRequest) returns (Operation) Undeploys a Model from an Endpoint, removing a DeployedModel from it, and freeing all resources it's using. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the endpoint resource: aiplatform.endpoints.undeploy For more information, see the IAM documentation.

UpdateEndpoint
rpc UpdateEndpoint(UpdateEndpointRequest) returns (Endpoint) Updates an Endpoint. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.endpoints.update For more information, see the IAM documentation.

UpdateEndpointLongRunning

rpc UpdateEndpointLongRunning(UpdateEndpointLongRunningRequest) returns (Operation) Updates an Endpoint with a long running operation. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.endpoints.update For more information, see the IAM documentation.

EvaluationAnalyticsService

Service for performing advanced analytics on evaluation data.

GenerateLossClusters
rpc GenerateLossClusters(GenerateLossClustersRequest) returns (Operation) Generates loss clusters from evaluation results. This is a statelss API method that would not modify the EvaluationSet resource. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

EvaluationService

Agent Platform Online Evaluation Service.

EvaluateDataset
rpc EvaluateDataset(EvaluateDatasetRequest) returns (Operation) Evaluates a dataset based on a set of given metrics. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

EvaluateInstances

rpc EvaluateInstances(EvaluateInstancesRequest) returns (EvaluateInstancesResponse) Evaluates instances based on a given metric. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the location resource: aiplatform.locations.evaluateInstances For more information, see the IAM documentation.

GenerateInstanceRubrics

rpc GenerateInstanceRubrics(GenerateInstanceRubricsRequest) returns (GenerateInstanceRubricsResponse) Generates rubrics for a given prompt. A rubric represents a single testable criterion for evaluation. One input prompt could have multiple rubrics This RPC allows users to get suggested rubrics based on provided prompt, which can then be reviewed and used for subsequent evaluations. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

ExampleStoreService

A service for managing and retrieving few-shot examples.

CreateExampleStore

rpc CreateExampleStore(CreateExampleStoreRequest) returns (Operation) Create an ExampleStore. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.exampleStores.create For more information, see the IAM documentation.

DeleteExampleStore

rpc DeleteExampleStore(DeleteExampleStoreRequest) returns (Operation) Delete an ExampleStore. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.exampleStores.delete For more information, see the IAM documentation.

FetchExamples

rpc FetchExamples(FetchExamplesRequest) returns (FetchExamplesResponse) Get Examples from the Example Store. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the exampleStore resource: aiplatform.exampleStores.readExample For more information, see the IAM documentation.

GetExampleStore

rpc GetExampleStore(GetExampleStoreRequest) returns (ExampleStore) Get an ExampleStore. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.exampleStores.get For more information, see the IAM documentation.

ListExampleStores

rpc ListExampleStores(ListExampleStoresRequest) returns (ListExampleStoresResponse) List ExampleStores in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.exampleStores.list For more information, see the IAM documentation.

RemoveExamples

rpc RemoveExamples(RemoveExamplesRequest) returns (RemoveExamplesResponse) Remove Examples from the Example Store. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the exampleStore resource: aiplatform.exampleStores.writeExample For more information, see the IAM documentation.

SearchExamples

rpc SearchExamples(SearchExamplesRequest) returns (SearchExamplesResponse) Search for similar Examples for given selection criteria. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the exampleStore resource: aiplatform.exampleStores.readExample For more information, see the IAM documentation.

UpdateExampleStore

rpc UpdateExampleStore(UpdateExampleStoreRequest) returns (Operation) Update an ExampleStore. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.exampleStores.update For more information, see the IAM documentation.

UpsertExamples

rpc UpsertExamples(UpsertExamplesRequest) returns (UpsertExamplesResponse) Create or update Examples in the Example Store. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the exampleStore resource: aiplatform.exampleStores.writeExample For more information, see the IAM documentation.

ExtensionExecutionService

A service for Extension execution.

ExecuteExtension

rpc ExecuteExtension(ExecuteExtensionRequest) returns (ExecuteExtensionResponse) Executes the request against a given extension. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.extensions.execute For more information, see the IAM documentation.

QueryExtension

rpc QueryExtension(QueryExtensionRequest) returns (QueryExtensionResponse) Queries an extension with a default controller. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.extensions.execute For more information, see the IAM documentation.

ExtensionRegistryService

A service for managing Agent Platform's Extension registry.

DeleteExtension
rpc DeleteExtension(DeleteExtensionRequest) returns (Operation) Deletes an Extension. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.extensions.delete For more information, see the IAM documentation.

GetExtension
rpc GetExtension(GetExtensionRequest) returns (Extension) Gets an Extension. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.extensions.get For more information, see the IAM documentation.

ImportExtension

rpc ImportExtension(ImportExtensionRequest) returns (Operation) Imports an Extension. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.extensions.import For more information, see the IAM documentation.

ListExtensions

rpc ListExtensions(ListExtensionsRequest) returns (ListExtensionsResponse) Lists Extensions in a location. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.extensions.list For more information, see the IAM documentation.

UpdateExtension
rpc UpdateExtension(UpdateExtensionRequest) returns (Extension) Updates an Extension. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.extensions.update For more information, see the IAM documentation.

FeatureOnlineStoreAdminService

The service that handles CRUD and List for resources for FeatureOnlineStore.

CreateFeatureOnlineStore

rpc CreateFeatureOnlineStore(CreateFeatureOnlineStoreRequest) returns (Operation) Creates a new FeatureOnlineStore in a given project and location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.featureOnlineStores.create For more information, see the IAM documentation.

CreateFeatureView

rpc CreateFeatureView(CreateFeatureViewRequest) returns (Operation) Creates a new FeatureView in a given FeatureOnlineStore. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.featureViews.create For more information, see the IAM documentation.

DeleteFeatureOnlineStore

rpc DeleteFeatureOnlineStore(DeleteFeatureOnlineStoreRequest) returns (Operation) Deletes a single FeatureOnlineStore. The FeatureOnlineStore must not contain any FeatureViews. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.featureOnlineStores.delete For more information, see the IAM documentation.

DeleteFeatureView

rpc DeleteFeatureView(DeleteFeatureViewRequest) returns (Operation) Deletes a single FeatureView. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.featureViews.delete For more information, see the IAM documentation.

GetFeatureOnlineStore

rpc GetFeatureOnlineStore(GetFeatureOnlineStoreRequest) returns (FeatureOnlineStore) Gets details of a single FeatureOnlineStore. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.featureOnlineStores.get For more information, see the IAM documentation.

GetFeatureView

rpc GetFeatureView(GetFeatureViewRequest) returns (FeatureView) Gets details of a single FeatureView. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.featureViews.get For more information, see the IAM documentation.

GetFeatureViewSync

rpc GetFeatureViewSync(GetFeatureViewSyncRequest) returns (FeatureViewSync) Gets details of a single FeatureViewSync. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.featureViewSyncs.get For more information, see the IAM documentation.

ListFeatureOnlineStores

rpc ListFeatureOnlineStores(ListFeatureOnlineStoresRequest) returns (ListFeatureOnlineStoresResponse) Lists FeatureOnlineStores in a given project and location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.featureOnlineStores.list For more information, see the IAM documentation.

ListFeatureViewSyncs

rpc ListFeatureViewSyncs(ListFeatureViewSyncsRequest) returns (ListFeatureViewSyncsResponse) Lists FeatureViewSyncs in a given FeatureView. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.featureViewSyncs.list For more information, see the IAM documentation.

ListFeatureViews

rpc ListFeatureViews(ListFeatureViewsRequest) returns (ListFeatureViewsResponse) Lists FeatureViews in a given FeatureOnlineStore. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.featureViews.list For more information, see the IAM documentation.

SyncFeatureView

rpc SyncFeatureView(SyncFeatureViewRequest) returns (SyncFeatureViewResponse) Triggers on-demand sync for the FeatureView. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the featureView resource: aiplatform.featureViews.sync For more information, see the IAM documentation.

UpdateFeatureOnlineStore

rpc UpdateFeatureOnlineStore(UpdateFeatureOnlineStoreRequest) returns (Operation) Updates the parameters of a single FeatureOnlineStore. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.featureOnlineStores.update For more information, see the IAM documentation.

UpdateFeatureView

rpc UpdateFeatureView(UpdateFeatureViewRequest) returns (Operation) Updates the parameters of a single FeatureView. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.featureViews.update For more information, see the IAM documentation.

FeatureOnlineStoreService

A service for fetching feature values from the online store.

FeatureViewDirectWrite

rpc FeatureViewDirectWrite(FeatureViewDirectWriteRequest) returns (FeatureViewDirectWriteResponse) Bidirectional streaming RPC to directly write to feature values in a feature view. Requests may not have a one-to-one mapping to responses and responses may be returned out-of-order to reduce latency. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the featureView resource: aiplatform.featureViews.directWrite For more information, see the IAM documentation.

FetchFeatureValues

rpc FetchFeatureValues(FetchFeatureValuesRequest) returns (FetchFeatureValuesResponse) Fetch feature values under a FeatureView. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the featureView resource: aiplatform.featureViews.fetchFeatureValues For more information, see the IAM documentation.

GenerateFetchAccessToken

rpc GenerateFetchAccessToken(GenerateFetchAccessTokenRequest) returns (GenerateFetchAccessTokenResponse) RPC to generate an access token for the given feature view. FeatureViews under the same FeatureOnlineStore share the same access token. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the featureView resource: aiplatform.featureViews.fetchFeatureValues For more information, see the IAM documentation.

SearchNearestEntities

rpc SearchNearestEntities(SearchNearestEntitiesRequest) returns (SearchNearestEntitiesResponse) Search the nearest entities under a FeatureView. Search only works for indexable feature view; if a feature view isn't indexable, returns Invalid argument response. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the featureView resource: aiplatform.featureViews.searchNearestEntities For more information, see the IAM documentation.

StreamingFetchFeatureValues

rpc StreamingFetchFeatureValues(StreamingFetchFeatureValuesRequest) returns (StreamingFetchFeatureValuesResponse) Bidirectional streaming RPC to fetch feature values under a FeatureView. Requests may not have a one-to-one mapping to responses and responses may be returned out-of-order to reduce latency. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the featureView resource: aiplatform.featureViews.fetchFeatureValues For more information, see the IAM documentation.

FeatureRegistryService

The service that handles CRUD and List for resources for FeatureRegistry.

BatchCreateFeatures

rpc BatchCreateFeatures(BatchCreateFeaturesRequest) returns (Operation) Creates a batch of Features in a given FeatureGroup. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.features.create For more information, see the IAM documentation.

CreateFeature

rpc CreateFeature(CreateFeatureRequest) returns (Operation) Creates a new Feature in a given FeatureGroup. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.features.create For more information, see the IAM documentation.

CreateFeatureGroup

rpc CreateFeatureGroup(CreateFeatureGroupRequest) returns (Operation) Creates a new FeatureGroup in a given project and location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.featureGroups.create For more information, see the IAM documentation.

CreateFeatureMonitor

rpc CreateFeatureMonitor(CreateFeatureMonitorRequest) returns (Operation) Creates a new FeatureMonitor in a given project, location and FeatureGroup. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.featureMonitors.create For more information, see the IAM documentation.

CreateFeatureMonitorJob

rpc CreateFeatureMonitorJob(CreateFeatureMonitorJobRequest) returns (FeatureMonitorJob) Creates a new feature monitor job. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.featureMonitorJobs.create For more information, see the IAM documentation.

DeleteFeature
rpc DeleteFeature(DeleteFeatureRequest) returns (Operation) Deletes a single Feature. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.features.delete For more information, see the IAM documentation.

DeleteFeatureGroup

rpc DeleteFeatureGroup(DeleteFeatureGroupRequest) returns (Operation) Deletes a single FeatureGroup. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.featureGroups.delete For more information, see the IAM documentation.

DeleteFeatureMonitor

rpc DeleteFeatureMonitor(DeleteFeatureMonitorRequest) returns (Operation) Deletes a single FeatureMonitor. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.featureMonitors.delete For more information, see the IAM documentation.

GetFeature
rpc GetFeature(GetFeatureRequest) returns (Feature) Gets details of a single Feature. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.features.get For more information, see the IAM documentation.

GetFeatureGroup

rpc GetFeatureGroup(GetFeatureGroupRequest) returns (FeatureGroup) Gets details of a single FeatureGroup. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.featureGroups.get For more information, see the IAM documentation.

GetFeatureMonitor

rpc GetFeatureMonitor(GetFeatureMonitorRequest) returns (FeatureMonitor) Gets details of a single FeatureMonitor. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.featureMonitors.get For more information, see the IAM documentation.

GetFeatureMonitorJob

rpc GetFeatureMonitorJob(GetFeatureMonitorJobRequest) returns (FeatureMonitorJob) Get a feature monitor job. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.featureMonitorJobs.get For more information, see the IAM documentation.

ListFeatureGroups

rpc ListFeatureGroups(ListFeatureGroupsRequest) returns (ListFeatureGroupsResponse) Lists FeatureGroups in a given project and location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.featureGroups.list For more information, see the IAM documentation.

ListFeatureMonitorJobs

rpc ListFeatureMonitorJobs(ListFeatureMonitorJobsRequest) returns (ListFeatureMonitorJobsResponse) List feature monitor jobs. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.featureMonitorJobs.list For more information, see the IAM documentation.

ListFeatureMonitors

rpc ListFeatureMonitors(ListFeatureMonitorsRequest) returns (ListFeatureMonitorsResponse) Lists FeatureGroups in a given project and location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.featureMonitors.list For more information, see the IAM documentation.

ListFeatures

rpc ListFeatures(ListFeaturesRequest) returns (ListFeaturesResponse) Lists Features in a given FeatureGroup. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.features.list For more information, see the IAM documentation.

UpdateFeature

rpc UpdateFeature(UpdateFeatureRequest) returns (Operation) Updates the parameters of a single Feature. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.features.update For more information, see the IAM documentation.

UpdateFeatureGroup

rpc UpdateFeatureGroup(UpdateFeatureGroupRequest) returns (Operation) Updates the parameters of a single FeatureGroup. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.featureGroups.update For more information, see the IAM documentation.

UpdateFeatureMonitor

rpc UpdateFeatureMonitor(UpdateFeatureMonitorRequest) returns (Operation) Updates the parameters of a single FeatureMonitor. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.featureMonitors.update For more information, see the IAM documentation.

FeaturestoreOnlineServingService

A service for serving online feature values.

ReadFeatureValues

rpc ReadFeatureValues(ReadFeatureValuesRequest) returns (ReadFeatureValuesResponse) Reads Feature values of a specific entity of an EntityType. For reading feature values of multiple entities of an EntityType, please use StreamingReadFeatureValues. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the entityType resource: aiplatform.entityTypes.readFeatureValues For more information, see the IAM documentation.

StreamingReadFeatureValues

rpc StreamingReadFeatureValues(StreamingReadFeatureValuesRequest) returns (ReadFeatureValuesResponse) Reads Feature values for multiple entities. Depending on their size, data for different entities may be broken up across multiple responses. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the entityType resource: aiplatform.entityTypes.streamingReadFeatureValues For more information, see the IAM documentation.

WriteFeatureValues

rpc WriteFeatureValues(WriteFeatureValuesRequest) returns (WriteFeatureValuesResponse) Writes Feature values of one or more entities of an EntityType. The Feature values are merged into existing entities if any. The Feature values to be written must have timestamp within the online storage retention. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the entityType resource: aiplatform.entityTypes.writeFeatureValues For more information, see the IAM documentation.

FeaturestoreService

The service that handles CRUD and List for resources for Featurestore.

BatchCreateFeatures

rpc BatchCreateFeatures(BatchCreateFeaturesRequest) returns (Operation) Creates a batch of Features in a given EntityType. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.features.create For more information, see the IAM documentation.

BatchReadFeatureValues

rpc BatchReadFeatureValues(BatchReadFeatureValuesRequest) returns (Operation) Batch reads Feature values from a Featurestore. This API enables batch reading Feature values, where each read instance in the batch may read Feature values of entities from one or more EntityTypes. Point-in-time correctness is guaranteed for Feature values of each read instance as of each instance's read timestamp. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the featurestore resource: aiplatform.featurestores.batchReadFeatureValues For more information, see the IAM documentation.

CreateEntityType

rpc CreateEntityType(CreateEntityTypeRequest) returns (Operation) Creates a new EntityType in a given Featurestore. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.entityTypes.create For more information, see the IAM documentation.

CreateFeature

rpc CreateFeature(CreateFeatureRequest) returns (Operation) Creates a new Feature in a given EntityType. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.features.create For more information, see the IAM documentation.

CreateFeaturestore

rpc CreateFeaturestore(CreateFeaturestoreRequest) returns (Operation) Creates a new Featurestore in a given project and location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.featurestores.create For more information, see the IAM documentation.

DeleteEntityType

rpc DeleteEntityType(DeleteEntityTypeRequest) returns (Operation) Deletes a single EntityType. The EntityType must not have any Features or force must be set to true for the request to succeed. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.entityTypes.delete For more information, see the IAM documentation.

DeleteFeature
rpc DeleteFeature(DeleteFeatureRequest) returns (Operation) Deletes a single Feature. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.features.delete For more information, see the IAM documentation.

DeleteFeatureValues

rpc DeleteFeatureValues(DeleteFeatureValuesRequest) returns (Operation) Delete Feature values from Featurestore. The progress of the deletion is tracked by the returned operation. The deleted feature values are guaranteed to be invisible to subsequent read operations after the operation is marked as successfully done. If a delete feature values operation fails, the feature values returned from reads and exports may be inconsistent. If consistency is required, the caller must retry the same delete request again and wait till the new operation returned is marked as successfully done. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the entityType resource: aiplatform.entityTypes.deleteFeatureValues For more information, see the IAM documentation.

DeleteFeaturestore

rpc DeleteFeaturestore(DeleteFeaturestoreRequest) returns (Operation) Deletes a single Featurestore. The Featurestore must not contain any EntityTypes or force must be set to true for the request to succeed. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.featurestores.delete For more information, see the IAM documentation.

ExportFeatureValues

rpc ExportFeatureValues(ExportFeatureValuesRequest) returns (Operation) Exports Feature values from all the entities of a target EntityType. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the entityType resource: aiplatform.entityTypes.exportFeatureValues For more information, see the IAM documentation.

GetEntityType

rpc GetEntityType(GetEntityTypeRequest) returns (EntityType) Gets details of a single EntityType. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.entityTypes.get For more information, see the IAM documentation.

GetFeature
rpc GetFeature(GetFeatureRequest) returns (Feature) Gets details of a single Feature. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.features.get For more information, see the IAM documentation.

GetFeaturestore

rpc GetFeaturestore(GetFeaturestoreRequest) returns (Featurestore) Gets details of a single Featurestore. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.featurestores.get For more information, see the IAM documentation.

ImportFeatureValues

rpc ImportFeatureValues(ImportFeatureValuesRequest) returns (Operation) Imports Feature values into the Featurestore from a source storage. The progress of the import is tracked by the returned operation. The imported features are guaranteed to be visible to subsequent read operations after the operation is marked as successfully done. If an import operation fails, the Feature values returned from reads and exports may be inconsistent. If consistency is required, the caller must retry the same import request again and wait till the new operation returned is marked as successfully done. There are also scenarios where the caller can cause inconsistency. Source data for import contains multiple distinct Feature values for the same entity ID and timestamp. Source is modified during an import. This includes adding, updating, or removing source data and/or metadata. Examples of updating metadata include but are not limited to changing storage location, storage class, or retention policy. Online serving cluster is under-provisioned. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the entityType resource: aiplatform.entityTypes.importFeatureValues For more information, see the IAM documentation.

ListEntityTypes

rpc ListEntityTypes(ListEntityTypesRequest) returns (ListEntityTypesResponse) Lists EntityTypes in a given Featurestore. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.entityTypes.list For more information, see the IAM documentation.

ListFeatures

rpc ListFeatures(ListFeaturesRequest) returns (ListFeaturesResponse) Lists Features in a given EntityType. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.features.list For more information, see the IAM documentation.

ListFeaturestores

rpc ListFeaturestores(ListFeaturestoresRequest) returns (ListFeaturestoresResponse) Lists Featurestores in a given project and location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.featurestores.list For more information, see the IAM documentation.

SearchFeatures

rpc SearchFeatures(SearchFeaturesRequest) returns (SearchFeaturesResponse) Searches Features matching a query in a given project. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the location resource: aiplatform.features.list For more information, see the IAM documentation.

UpdateEntityType

rpc UpdateEntityType(UpdateEntityTypeRequest) returns (EntityType) Updates the parameters of a single EntityType. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.entityTypes.update For more information, see the IAM documentation.

UpdateFeature

rpc UpdateFeature(UpdateFeatureRequest) returns (Feature) Updates the parameters of a single Feature. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.features.update For more information, see the IAM documentation.

UpdateFeaturestore

rpc UpdateFeaturestore(UpdateFeaturestoreRequest) returns (Operation) Updates the parameters of a single Featurestore. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.featurestores.update For more information, see the IAM documentation.

GenAiCacheService

Service for managing Agent Platform's CachedContent resource.

CreateCachedContent

rpc CreateCachedContent(CreateCachedContentRequest) returns (CachedContent) Creates cached content, this call will initialize the cached content in the data storage, and users need to pay for the cache data storage. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.cachedContents.create For more information, see the IAM documentation.

DeleteCachedContent

rpc DeleteCachedContent(DeleteCachedContentRequest) returns (Empty) Deletes cached content Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.cachedContents.delete For more information, see the IAM documentation.

GetCachedContent

rpc GetCachedContent(GetCachedContentRequest) returns (CachedContent) Gets cached content configurations Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.cachedContents.get For more information, see the IAM documentation.

ListCachedContents

rpc ListCachedContents(ListCachedContentsRequest) returns (ListCachedContentsResponse) Lists cached contents in a project Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.cachedContents.list For more information, see the IAM documentation.

UpdateCachedContent

rpc UpdateCachedContent(UpdateCachedContentRequest) returns (CachedContent) Updates cached content configurations Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.cachedContents.update For more information, see the IAM documentation.

GenAiTuningService

A service for creating and managing GenAI Tuning Jobs.

A tuning job is a process that takes a base model and further trains it on a user-provided dataset. This process, also known as fine-tuning, adapts the model to perform better on specific tasks. The result of a successful tuning job is a new "tuned model" that can be deployed and used for inference.

CancelTuningJob

rpc CancelTuningJob(CancelTuningJobRequest) returns (Empty) Cancels a tuning job. Starts an asynchronous cancellation request. The server makes a best effort to cancel the job, but success is not guaranteed. Clients can use GenAiTuningService.GetTuningJob or other methods to check whether the cancellation succeeded or whether the job completed despite cancellation. On successful cancellation, the tuning job is not deleted. Instead, its state is set to CANCELLED, and error is set to a status with a google.rpc.Status.code of 1, corresponding to Code.CANCELLED. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.tuningJobs.cancel For more information, see the IAM documentation.

CreateTuningJob

rpc CreateTuningJob(CreateTuningJobRequest) returns (TuningJob) Creates a tuning job. A created tuning job will be subsequently executed to start the model tuning process. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.tuningJobs.create For more information, see the IAM documentation.

GetTuningJob
rpc GetTuningJob(GetTuningJobRequest) returns (TuningJob) Gets a tuning job. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.tuningJobs.get For more information, see the IAM documentation.

ListTuningJobs

rpc ListTuningJobs(ListTuningJobsRequest) returns (ListTuningJobsResponse) Lists tuning jobs in a location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.tuningJobs.list For more information, see the IAM documentation.

RebaseTunedModel

rpc RebaseTunedModel(RebaseTunedModelRequest) returns (Operation) Rebase a tuned model. A rebase operation takes a model that was previously tuned on a base model version, and retunes it on a new base model version. The rebase operation creates a new tuning job and a new tuned model. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.tuningJobs.create For more information, see the IAM documentation.

IndexEndpointService

A service for managing Agent Platform's IndexEndpoints.

CreateIndexEndpoint

rpc CreateIndexEndpoint(CreateIndexEndpointRequest) returns (Operation) Creates an IndexEndpoint. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.indexEndpoints.create For more information, see the IAM documentation.

DeleteIndexEndpoint

rpc DeleteIndexEndpoint(DeleteIndexEndpointRequest) returns (Operation) Deletes an IndexEndpoint. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.indexEndpoints.delete For more information, see the IAM documentation.

DeployIndex

rpc DeployIndex(DeployIndexRequest) returns (Operation) Deploys an Index into this IndexEndpoint, creating a DeployedIndex within it. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the indexEndpoint resource: aiplatform.indexEndpoints.deploy For more information, see the IAM documentation.

GetIndexEndpoint

rpc GetIndexEndpoint(GetIndexEndpointRequest) returns (IndexEndpoint) Gets an IndexEndpoint. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.indexEndpoints.get For more information, see the IAM documentation.

ListIndexEndpoints

rpc ListIndexEndpoints(ListIndexEndpointsRequest) returns (ListIndexEndpointsResponse) Lists IndexEndpoints in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.indexEndpoints.list For more information, see the IAM documentation.

MutateDeployedIndex

rpc MutateDeployedIndex(MutateDeployedIndexRequest) returns (Operation) Update an existing DeployedIndex under an IndexEndpoint. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the indexEndpoint resource: aiplatform.indexEndpoints.deploy For more information, see the IAM documentation.

UndeployIndex

rpc UndeployIndex(UndeployIndexRequest) returns (Operation) Undeploys an Index from an IndexEndpoint, removing a DeployedIndex from it, and freeing all resources it's using. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the indexEndpoint resource: aiplatform.indexEndpoints.undeploy For more information, see the IAM documentation.

UpdateIndexEndpoint

rpc UpdateIndexEndpoint(UpdateIndexEndpointRequest) returns (IndexEndpoint) Updates an IndexEndpoint. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.indexEndpoints.update For more information, see the IAM documentation.

IndexService

A service for creating and managing Agent Platform's Index resources.

CreateIndex
rpc CreateIndex(CreateIndexRequest) returns (Operation) Creates an Index. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.indexes.create For more information, see the IAM documentation.

DeleteIndex

rpc DeleteIndex(DeleteIndexRequest) returns (Operation) Deletes an Index. An Index can only be deleted when all its DeployedIndexes had been undeployed. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.indexes.delete For more information, see the IAM documentation.

GetIndex
rpc GetIndex(GetIndexRequest) returns (Index) Gets an Index. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.indexes.get For more information, see the IAM documentation.

ImportIndex

rpc ImportIndex(ImportIndexRequest) returns (Operation) Imports an Index from an external source (e.g., BigQuery). Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.indexes.update For more information, see the IAM documentation.

ListIndexes

rpc ListIndexes(ListIndexesRequest) returns (ListIndexesResponse) Lists Indexes in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.indexes.list For more information, see the IAM documentation.

RemoveDatapoints

rpc RemoveDatapoints(RemoveDatapointsRequest) returns (RemoveDatapointsResponse) Remove Datapoints from an Index. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the index resource: aiplatform.indexes.update For more information, see the IAM documentation.

UpdateIndex
rpc UpdateIndex(UpdateIndexRequest) returns (Operation) Updates an Index. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.indexes.update For more information, see the IAM documentation.

UpsertDatapoints

rpc UpsertDatapoints(UpsertDatapointsRequest) returns (UpsertDatapointsResponse) Add/update Datapoints into an Index. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the index resource: aiplatform.indexes.update For more information, see the IAM documentation.

JobService

A service for creating and managing Agent Platform's jobs.

CancelBatchPredictionJob

rpc CancelBatchPredictionJob(CancelBatchPredictionJobRequest) returns (Empty) Cancels a BatchPredictionJob. Starts asynchronous cancellation on the BatchPredictionJob. The server makes the best effort to cancel the job, but success is not guaranteed. Clients can use JobService.GetBatchPredictionJob or other methods to check whether the cancellation succeeded or whether the job completed despite cancellation. On a successful cancellation, the BatchPredictionJob is not deleted;instead its BatchPredictionJob.state is set to CANCELLED. Any files already outputted by the job are not deleted. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.batchPredictionJobs.cancel For more information, see the IAM documentation.

CancelCustomJob

rpc CancelCustomJob(CancelCustomJobRequest) returns (Empty) Cancels a CustomJob. Starts asynchronous cancellation on the CustomJob. The server makes a best effort to cancel the job, but success is not guaranteed. Clients can use JobService.GetCustomJob or other methods to check whether the cancellation succeeded or whether the job completed despite cancellation. On successful cancellation, the CustomJob is not deleted; instead it becomes a job with a CustomJob.error value with a google.rpc.Status.code of 1, corresponding to Code.CANCELLED, and CustomJob.state is set to CANCELLED. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.customJobs.cancel For more information, see the IAM documentation.

CancelHyperparameterTuningJob

rpc CancelHyperparameterTuningJob(CancelHyperparameterTuningJobRequest) returns (Empty) Cancels a HyperparameterTuningJob. Starts asynchronous cancellation on the HyperparameterTuningJob. The server makes a best effort to cancel the job, but success is not guaranteed. Clients can use JobService.GetHyperparameterTuningJob or other methods to check whether the cancellation succeeded or whether the job completed despite cancellation. On successful cancellation, the HyperparameterTuningJob is not deleted; instead it becomes a job with a HyperparameterTuningJob.error value with a google.rpc.Status.code of 1, corresponding to Code.CANCELLED, and HyperparameterTuningJob.state is set to CANCELLED. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.hyperparameterTuningJobs.cancel For more information, see the IAM documentation.

CreateBatchPredictionJob

rpc CreateBatchPredictionJob(CreateBatchPredictionJobRequest) returns (BatchPredictionJob) Creates a BatchPredictionJob. A BatchPredictionJob once created will right away be attempted to start. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.batchPredictionJobs.create For more information, see the IAM documentation.

CreateCustomJob

rpc CreateCustomJob(CreateCustomJobRequest) returns (CustomJob) Creates a CustomJob. A created CustomJob right away will be attempted to be run. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.customJobs.create For more information, see the IAM documentation.

CreateHyperparameterTuningJob

rpc CreateHyperparameterTuningJob(CreateHyperparameterTuningJobRequest) returns (HyperparameterTuningJob) Creates a HyperparameterTuningJob Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.hyperparameterTuningJobs.create For more information, see the IAM documentation.

CreateModelDeploymentMonitoringJob

rpc CreateModelDeploymentMonitoringJob(CreateModelDeploymentMonitoringJobRequest) returns (ModelDeploymentMonitoringJob) Creates a ModelDeploymentMonitoringJob. It will run periodically on a configured interval. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.modelDeploymentMonitoringJobs.create For more information, see the IAM documentation.

DeleteBatchPredictionJob

rpc DeleteBatchPredictionJob(DeleteBatchPredictionJobRequest) returns (Operation) Deletes a BatchPredictionJob. Can only be called on jobs that already finished. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.batchPredictionJobs.delete For more information, see the IAM documentation.

DeleteCustomJob
rpc DeleteCustomJob(DeleteCustomJobRequest) returns (Operation) Deletes a CustomJob. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.customJobs.delete For more information, see the IAM documentation.

DeleteHyperparameterTuningJob

rpc DeleteHyperparameterTuningJob(DeleteHyperparameterTuningJobRequest) returns (Operation) Deletes a HyperparameterTuningJob. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.hyperparameterTuningJobs.delete For more information, see the IAM documentation.

DeleteModelDeploymentMonitoringJob

rpc DeleteModelDeploymentMonitoringJob(DeleteModelDeploymentMonitoringJobRequest) returns (Operation) Deletes a ModelDeploymentMonitoringJob. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.modelDeploymentMonitoringJobs.delete For more information, see the IAM documentation.

GetBatchPredictionJob

rpc GetBatchPredictionJob(GetBatchPredictionJobRequest) returns (BatchPredictionJob) Gets a BatchPredictionJob Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.batchPredictionJobs.get For more information, see the IAM documentation.

GetCustomJob
rpc GetCustomJob(GetCustomJobRequest) returns (CustomJob) Gets a CustomJob. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.customJobs.get For more information, see the IAM documentation.

GetHyperparameterTuningJob

rpc GetHyperparameterTuningJob(GetHyperparameterTuningJobRequest) returns (HyperparameterTuningJob) Gets a HyperparameterTuningJob Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.hyperparameterTuningJobs.get For more information, see the IAM documentation.

GetModelDeploymentMonitoringJob

rpc GetModelDeploymentMonitoringJob(GetModelDeploymentMonitoringJobRequest) returns (ModelDeploymentMonitoringJob) Gets a ModelDeploymentMonitoringJob. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.modelDeploymentMonitoringJobs.get For more information, see the IAM documentation.

ListBatchPredictionJobs

rpc ListBatchPredictionJobs(ListBatchPredictionJobsRequest) returns (ListBatchPredictionJobsResponse) Lists BatchPredictionJobs in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.batchPredictionJobs.list For more information, see the IAM documentation.

ListCustomJobs

rpc ListCustomJobs(ListCustomJobsRequest) returns (ListCustomJobsResponse) Lists CustomJobs in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.customJobs.list For more information, see the IAM documentation.

ListHyperparameterTuningJobs

rpc ListHyperparameterTuningJobs(ListHyperparameterTuningJobsRequest) returns (ListHyperparameterTuningJobsResponse) Lists HyperparameterTuningJobs in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.hyperparameterTuningJobs.list For more information, see the IAM documentation.

ListModelDeploymentMonitoringJobs

rpc ListModelDeploymentMonitoringJobs(ListModelDeploymentMonitoringJobsRequest) returns (ListModelDeploymentMonitoringJobsResponse) Lists ModelDeploymentMonitoringJobs in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.modelDeploymentMonitoringJobs.list For more information, see the IAM documentation.

PauseModelDeploymentMonitoringJob

rpc PauseModelDeploymentMonitoringJob(PauseModelDeploymentMonitoringJobRequest) returns (Empty) Pauses a ModelDeploymentMonitoringJob. If the job is running, the server makes a best effort to cancel the job. Will mark ModelDeploymentMonitoringJob.state to 'PAUSED'. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.modelDeploymentMonitoringJobs.pause For more information, see the IAM documentation.

ResumeModelDeploymentMonitoringJob

rpc ResumeModelDeploymentMonitoringJob(ResumeModelDeploymentMonitoringJobRequest) returns (Empty) Resumes a paused ModelDeploymentMonitoringJob. It will start to run from next scheduled time. A deleted ModelDeploymentMonitoringJob can't be resumed. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.modelDeploymentMonitoringJobs.resume For more information, see the IAM documentation.

SearchModelDeploymentMonitoringStatsAnomalies

rpc SearchModelDeploymentMonitoringStatsAnomalies(SearchModelDeploymentMonitoringStatsAnomaliesRequest) returns (SearchModelDeploymentMonitoringStatsAnomaliesResponse) Searches Model Monitoring Statistics generated within a given time window. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the modelDeploymentMonitoringJob resource: aiplatform.modelDeploymentMonitoringJobs.searchStatsAnomalies For more information, see the IAM documentation.

UpdateModelDeploymentMonitoringJob

rpc UpdateModelDeploymentMonitoringJob(UpdateModelDeploymentMonitoringJobRequest) returns (Operation) Updates a ModelDeploymentMonitoringJob. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.modelDeploymentMonitoringJobs.update For more information, see the IAM documentation.

LlmUtilityService

Service for LLM related utility functions.

ComputeTokens

rpc ComputeTokens(ComputeTokensRequest) returns (ComputeTokensResponse) Return a list of tokens based on the input text. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the endpoint resource: aiplatform.endpoints.predict For more information, see the IAM documentation.

MatchService

MatchService is a Google managed service for efficient vector similarity search at scale.

MemoryBankService

A service for managing memories for LLM applications.

CreateMemory
rpc CreateMemory(CreateMemoryRequest) returns (Operation) Create a Memory. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

DeleteMemory
rpc DeleteMemory(DeleteMemoryRequest) returns (Operation) Delete a Memory. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

GenerateMemories
rpc GenerateMemories(GenerateMemoriesRequest) returns (Operation) Generate memories. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

GetMemory
rpc GetMemory(GetMemoryRequest) returns (Memory) Get a Memory. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

IngestEvents
rpc IngestEvents(IngestEventsRequest) returns (Operation) Ingests events for a Memory Bank. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

ListMemories
rpc ListMemories(ListMemoriesRequest) returns (ListMemoriesResponse) List Memories. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.memories.list For more information, see the IAM documentation.

RetrieveMemories
rpc RetrieveMemories(RetrieveMemoriesRequest) returns (RetrieveMemoriesResponse) Retrieve memories. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

RetrieveProfiles
rpc RetrieveProfiles(RetrieveProfilesRequest) returns (RetrieveProfilesResponse) Retrieves profiles. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

UpdateMemory
rpc UpdateMemory(UpdateMemoryRequest) returns (Operation) Update a Memory. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

MetadataService

Service for reading and writing metadata entries.

AddContextArtifactsAndExecutions

rpc AddContextArtifactsAndExecutions(AddContextArtifactsAndExecutionsRequest) returns (AddContextArtifactsAndExecutionsResponse) Adds a set of Artifacts and Executions to a Context. If any of the Artifacts or Executions have already been added to a Context, they are simply skipped. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the context resource: aiplatform.contexts.addContextArtifactsAndExecutions For more information, see the IAM documentation.

AddContextChildren

rpc AddContextChildren(AddContextChildrenRequest) returns (AddContextChildrenResponse) Adds a set of Contexts as children to a parent Context. If any of the child Contexts have already been added to the parent Context, they are simply skipped. If this call would create a cycle or cause any Context to have more than 10 parents, the request will fail with an INVALID_ARGUMENT error. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the context resource: aiplatform.contexts.addContextChildren For more information, see the IAM documentation.

AddExecutionEvents

rpc AddExecutionEvents(AddExecutionEventsRequest) returns (AddExecutionEventsResponse) Adds Events to the specified Execution. An Event indicates whether an Artifact was used as an input or output for an Execution. If an Event already exists between the Execution and the Artifact, the Event is skipped. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the execution resource: aiplatform.executions.addExecutionEvents For more information, see the IAM documentation.

CreateArtifact

rpc CreateArtifact(CreateArtifactRequest) returns (Artifact) Creates an Artifact associated with a MetadataStore. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.artifacts.create For more information, see the IAM documentation.

CreateContext

rpc CreateContext(CreateContextRequest) returns (Context) Creates a Context associated with a MetadataStore. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.contexts.create For more information, see the IAM documentation.

CreateExecution

rpc CreateExecution(CreateExecutionRequest) returns (Execution) Creates an Execution associated with a MetadataStore. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.executions.create For more information, see the IAM documentation.

CreateMetadataSchema

rpc CreateMetadataSchema(CreateMetadataSchemaRequest) returns (MetadataSchema) Creates a MetadataSchema. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.metadataSchemas.create For more information, see the IAM documentation.

CreateMetadataStore

rpc CreateMetadataStore(CreateMetadataStoreRequest) returns (Operation) Initializes a MetadataStore, including allocation of resources. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.metadataStores.create For more information, see the IAM documentation.

DeleteArtifact
rpc DeleteArtifact(DeleteArtifactRequest) returns (Operation) Deletes an Artifact. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.artifacts.delete For more information, see the IAM documentation.

DeleteContext
rpc DeleteContext(DeleteContextRequest) returns (Operation) Deletes a stored Context. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.contexts.delete For more information, see the IAM documentation.

DeleteExecution
rpc DeleteExecution(DeleteExecutionRequest) returns (Operation) Deletes an Execution. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.executions.delete For more information, see the IAM documentation.

DeleteMetadataStore

rpc DeleteMetadataStore(DeleteMetadataStoreRequest) returns (Operation) Deletes a single MetadataStore and all its child resources (Artifacts, Executions, and Contexts). Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.metadataStores.delete For more information, see the IAM documentation.

GetArtifact
rpc GetArtifact(GetArtifactRequest) returns (Artifact) Retrieves a specific Artifact. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.artifacts.get For more information, see the IAM documentation.

GetContext
rpc GetContext(GetContextRequest) returns (Context) Retrieves a specific Context. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.contexts.get For more information, see the IAM documentation.

GetExecution
rpc GetExecution(GetExecutionRequest) returns (Execution) Retrieves a specific Execution. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.executions.get For more information, see the IAM documentation.

GetMetadataSchema

rpc GetMetadataSchema(GetMetadataSchemaRequest) returns (MetadataSchema) Retrieves a specific MetadataSchema. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.metadataSchemas.get For more information, see the IAM documentation.

GetMetadataStore

rpc GetMetadataStore(GetMetadataStoreRequest) returns (MetadataStore) Retrieves a specific MetadataStore. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.metadataStores.get For more information, see the IAM documentation.

ListArtifacts

rpc ListArtifacts(ListArtifactsRequest) returns (ListArtifactsResponse) Lists Artifacts in the MetadataStore. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.artifacts.list For more information, see the IAM documentation.

ListContexts

rpc ListContexts(ListContextsRequest) returns (ListContextsResponse) Lists Contexts on the MetadataStore. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.contexts.list For more information, see the IAM documentation.

ListExecutions

rpc ListExecutions(ListExecutionsRequest) returns (ListExecutionsResponse) Lists Executions in the MetadataStore. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.executions.list For more information, see the IAM documentation.

ListMetadataSchemas

rpc ListMetadataSchemas(ListMetadataSchemasRequest) returns (ListMetadataSchemasResponse) Lists MetadataSchemas. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.metadataSchemas.list For more information, see the IAM documentation.

ListMetadataStores

rpc ListMetadataStores(ListMetadataStoresRequest) returns (ListMetadataStoresResponse) Lists MetadataStores for a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.metadataStores.list For more information, see the IAM documentation.

PurgeArtifacts
rpc PurgeArtifacts(PurgeArtifactsRequest) returns (Operation) Purges Artifacts. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.artifacts.delete For more information, see the IAM documentation.

PurgeContexts
rpc PurgeContexts(PurgeContextsRequest) returns (Operation) Purges Contexts. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.contexts.delete For more information, see the IAM documentation.

PurgeExecutions
rpc PurgeExecutions(PurgeExecutionsRequest) returns (Operation) Purges Executions. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.executions.delete For more information, see the IAM documentation.

QueryArtifactLineageSubgraph

rpc QueryArtifactLineageSubgraph(QueryArtifactLineageSubgraphRequest) returns (LineageSubgraph) Retrieves lineage of an Artifact represented through Artifacts and Executions connected by Event edges and returned as a LineageSubgraph. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the artifact resource: aiplatform.artifacts.get For more information, see the IAM documentation.

QueryContextLineageSubgraph

rpc QueryContextLineageSubgraph(QueryContextLineageSubgraphRequest) returns (LineageSubgraph) Retrieves Artifacts and Executions within the specified Context, connected by Event edges and returned as a LineageSubgraph. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the context resource: aiplatform.contexts.queryContextLineageSubgraph For more information, see the IAM documentation.

QueryExecutionInputsAndOutputs

rpc QueryExecutionInputsAndOutputs(QueryExecutionInputsAndOutputsRequest) returns (LineageSubgraph) Obtains the set of input and output Artifacts for this Execution, in the form of LineageSubgraph that also contains the Execution and connecting Events. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the execution resource: aiplatform.executions.queryExecutionInputsAndOutputs For more information, see the IAM documentation.

RemoveContextChildren

rpc RemoveContextChildren(RemoveContextChildrenRequest) returns (RemoveContextChildrenResponse) Remove a set of children contexts from a parent Context. If any of the child Contexts were NOT added to the parent Context, they are simply skipped. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the context resource: aiplatform.contexts.addContextChildren For more information, see the IAM documentation.

UpdateArtifact
rpc UpdateArtifact(UpdateArtifactRequest) returns (Artifact) Updates a stored Artifact. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.artifacts.update For more information, see the IAM documentation.

UpdateContext
rpc UpdateContext(UpdateContextRequest) returns (Context) Updates a stored Context. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.contexts.update For more information, see the IAM documentation.

UpdateExecution

rpc UpdateExecution(UpdateExecutionRequest) returns (Execution) Updates a stored Execution. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.executions.update For more information, see the IAM documentation.

MigrationService

A service that migrates resources from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Agent Platform.

BatchMigrateResources

rpc BatchMigrateResources(BatchMigrateResourcesRequest) returns (Operation) Batch migrates resources from ml.googleapis.com, automl.googleapis.com, and datalabeling.googleapis.com to Agent Platform. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.migratableResources.migrate For more information, see the IAM documentation.

SearchMigratableResources

rpc SearchMigratableResources(SearchMigratableResourcesRequest) returns (SearchMigratableResourcesResponse) Searches all of the resources in automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com that can be migrated to Agent Platform's given location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.migratableResources.search For more information, see the IAM documentation.

ModelGardenService

The interface of Model Garden Service.

AcceptPublisherModelEula

rpc AcceptPublisherModelEula(AcceptPublisherModelEulaRequest) returns (PublisherModelEulaAcceptance) Accepts the EULA acceptance status of a publisher model. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.endpoints.create For more information, see the IAM documentation.

CheckPublisherModelEulaAcceptance

rpc CheckPublisherModelEulaAcceptance(CheckPublisherModelEulaAcceptanceRequest) returns (PublisherModelEulaAcceptance) Checks the EULA acceptance status of a publisher model. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.endpoints.get For more information, see the IAM documentation.

Deploy

rpc Deploy(DeployRequest) returns (Operation) Deploys a model to a new endpoint. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permissions on the destination resource: aiplatform.endpoints.create aiplatform.endpoints.deploy aiplatform.models.upload For more information, see the IAM documentation.

DeployPublisherModel

This item is deprecated! rpc DeployPublisherModel(DeployPublisherModelRequest) returns (Operation) Deploys publisher models. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permissions on the destination resource: aiplatform.endpoints.create aiplatform.endpoints.deploy aiplatform.models.upload For more information, see the IAM documentation.

ExportPublisherModel
rpc ExportPublisherModel(ExportPublisherModelRequest) returns (Operation) Exports a publisher model to a user provided Google Cloud Storage bucket. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

GetPublisherModel
rpc GetPublisherModel(GetPublisherModelRequest) returns (PublisherModel) Gets a Model Garden publisher model. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

ListPublisherModels
rpc ListPublisherModels(ListPublisherModelsRequest) returns (ListPublisherModelsResponse) Lists publisher models in Model Garden. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

ModelMonitoringService

A service for creating and managing Agent Platform Model moitoring. This includes ModelMonitor resources, ModelMonitoringJob resources.

CreateModelMonitor

rpc CreateModelMonitor(CreateModelMonitorRequest) returns (Operation) Creates a ModelMonitor. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.modelMonitors.create For more information, see the IAM documentation.

CreateModelMonitoringJob

rpc CreateModelMonitoringJob(CreateModelMonitoringJobRequest) returns (ModelMonitoringJob) Creates a ModelMonitoringJob. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.modelMonitoringJobs.create For more information, see the IAM documentation.

DeleteModelMonitor

rpc DeleteModelMonitor(DeleteModelMonitorRequest) returns (Operation) Deletes a ModelMonitor. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.modelMonitors.delete For more information, see the IAM documentation.

DeleteModelMonitoringJob

rpc DeleteModelMonitoringJob(DeleteModelMonitoringJobRequest) returns (Operation) Deletes a ModelMonitoringJob. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.modelMonitoringJobs.delete For more information, see the IAM documentation.

GetModelMonitor

rpc GetModelMonitor(GetModelMonitorRequest) returns (ModelMonitor) Gets a ModelMonitor. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.modelMonitors.get For more information, see the IAM documentation.

GetModelMonitoringJob

rpc GetModelMonitoringJob(GetModelMonitoringJobRequest) returns (ModelMonitoringJob) Gets a ModelMonitoringJob. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.modelMonitoringJobs.get For more information, see the IAM documentation.

ListModelMonitoringJobs

rpc ListModelMonitoringJobs(ListModelMonitoringJobsRequest) returns (ListModelMonitoringJobsResponse) Lists ModelMonitoringJobs. Callers may choose to read across multiple Monitors as per AIP-159 by using '-' (the hyphen or dash character) as a wildcard character instead of modelMonitor id in the parent. Format projects/{project_id}/locations/{location}/moodelMonitors/-/modelMonitoringJobs Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.modelMonitoringJobs.list For more information, see the IAM documentation.

ListModelMonitors

rpc ListModelMonitors(ListModelMonitorsRequest) returns (ListModelMonitorsResponse) Lists ModelMonitors in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.modelMonitors.list For more information, see the IAM documentation.

SearchModelMonitoringAlerts

rpc SearchModelMonitoringAlerts(SearchModelMonitoringAlertsRequest) returns (SearchModelMonitoringAlertsResponse) Returns the Model Monitoring alerts. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the modelMonitor resource: aiplatform.modelMonitors.searchModelMonitoringAlerts For more information, see the IAM documentation.

SearchModelMonitoringStats

rpc SearchModelMonitoringStats(SearchModelMonitoringStatsRequest) returns (SearchModelMonitoringStatsResponse) Searches Model Monitoring Stats generated within a given time window. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the modelMonitor resource: aiplatform.modelMonitors.searchModelMonitoringStats For more information, see the IAM documentation.

UpdateModelMonitor

rpc UpdateModelMonitor(UpdateModelMonitorRequest) returns (Operation) Updates a ModelMonitor. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.modelMonitors.update For more information, see the IAM documentation.

ModelService

A service for managing Agent Platform's machine learning Models.

BatchImportEvaluatedAnnotations

rpc BatchImportEvaluatedAnnotations(BatchImportEvaluatedAnnotationsRequest) returns (BatchImportEvaluatedAnnotationsResponse) Imports a list of externally generated EvaluatedAnnotations. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.modelEvaluationSlices.import For more information, see the IAM documentation.

BatchImportModelEvaluationSlices

rpc BatchImportModelEvaluationSlices(BatchImportModelEvaluationSlicesRequest) returns (BatchImportModelEvaluationSlicesResponse) Imports a list of externally generated ModelEvaluationSlice. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.modelEvaluationSlices.import For more information, see the IAM documentation.

CopyModel

rpc CopyModel(CopyModelRequest) returns (Operation) Copies an already existing Agent Platform Model into the specified Location. The source Model must exist in the same Project. When copying custom Models, the users themselves are responsible for Model.metadata content to be region-agnostic, as well as making sure that any resources (e.g. files) it depends on remain accessible. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.models.upload For more information, see the IAM documentation.

DeleteModel

rpc DeleteModel(DeleteModelRequest) returns (Operation) Deletes a Model. A model cannot be deleted if any Endpoint resource has a DeployedModel based on the model in its deployed_models field. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.models.delete For more information, see the IAM documentation.

DeleteModelVersion

rpc DeleteModelVersion(DeleteModelVersionRequest) returns (Operation) Deletes a Model version. Model version can only be deleted if there are no DeployedModels created from it. Deleting the only version in the Model is not allowed. Use DeleteModel for deleting the Model instead. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.models.delete For more information, see the IAM documentation.

ExportModel

rpc ExportModel(ExportModelRequest) returns (Operation) Exports a trained, exportable Model to a location specified by the user. A Model is considered to be exportable if it has at least one supported export format. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.models.export For more information, see the IAM documentation.

GetModel
rpc GetModel(GetModelRequest) returns (Model) Gets a Model. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.models.get For more information, see the IAM documentation.

GetModelEvaluation

rpc GetModelEvaluation(GetModelEvaluationRequest) returns (ModelEvaluation) Gets a ModelEvaluation. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.modelEvaluations.get For more information, see the IAM documentation.

GetModelEvaluationSlice

rpc GetModelEvaluationSlice(GetModelEvaluationSliceRequest) returns (ModelEvaluationSlice) Gets a ModelEvaluationSlice. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.modelEvaluationSlices.get For more information, see the IAM documentation.

ImportModelEvaluation

rpc ImportModelEvaluation(ImportModelEvaluationRequest) returns (ModelEvaluation) Imports an externally generated ModelEvaluation. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.modelEvaluations.import For more information, see the IAM documentation.

ListModelEvaluationSlices

rpc ListModelEvaluationSlices(ListModelEvaluationSlicesRequest) returns (ListModelEvaluationSlicesResponse) Lists ModelEvaluationSlices in a ModelEvaluation. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.modelEvaluationSlices.list For more information, see the IAM documentation.

ListModelEvaluations

rpc ListModelEvaluations(ListModelEvaluationsRequest) returns (ListModelEvaluationsResponse) Lists ModelEvaluations in a Model. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.modelEvaluations.list For more information, see the IAM documentation.

ListModelVersionCheckpoints

rpc ListModelVersionCheckpoints(ListModelVersionCheckpointsRequest) returns (ListModelVersionCheckpointsResponse) Lists checkpoints of the specified model version. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.models.get For more information, see the IAM documentation.

ListModelVersions

rpc ListModelVersions(ListModelVersionsRequest) returns (ListModelVersionsResponse) Lists versions of the specified model. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.models.get For more information, see the IAM documentation.

ListModels
rpc ListModels(ListModelsRequest) returns (ListModelsResponse) Lists Models in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.models.list For more information, see the IAM documentation.

MergeVersionAliases

rpc MergeVersionAliases(MergeVersionAliasesRequest) returns (Model) Merges a set of aliases for a Model version. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.models.update For more information, see the IAM documentation.

RecommendSpec
rpc RecommendSpec(RecommendSpecRequest) returns (RecommendSpecResponse) Gets a Model's spec recommendations. This API is called by UI, SDK, and internal. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

UpdateExplanationDataset

rpc UpdateExplanationDataset(UpdateExplanationDatasetRequest) returns (Operation) Incrementally update the dataset used for an examples model. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the model resource: aiplatform.models.update For more information, see the IAM documentation.

UpdateModel
rpc UpdateModel(UpdateModelRequest) returns (Model) Updates a Model. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.models.update For more information, see the IAM documentation.

UploadModel

rpc UploadModel(UploadModelRequest) returns (Operation) Uploads a Model artifact into Agent Platform. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.models.upload For more information, see the IAM documentation.

NotebookService

The interface for Vertex Notebook service (a.k.a. Colab on Workbench).

AssignNotebookRuntime

rpc AssignNotebookRuntime(AssignNotebookRuntimeRequest) returns (Operation) Assigns a NotebookRuntime to a user for a particular Notebook file. This method will either returns an existing assignment or generates a new one. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.notebookRuntimes.assign For more information, see the IAM documentation.

CreateNotebookExecutionJob

rpc CreateNotebookExecutionJob(CreateNotebookExecutionJobRequest) returns (Operation) Creates a NotebookExecutionJob. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.notebookExecutionJobs.create For more information, see the IAM documentation.

CreateNotebookRuntimeTemplate

rpc CreateNotebookRuntimeTemplate(CreateNotebookRuntimeTemplateRequest) returns (Operation) Creates a NotebookRuntimeTemplate. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.notebookRuntimeTemplates.create For more information, see the IAM documentation.

DeleteNotebookExecutionJob

rpc DeleteNotebookExecutionJob(DeleteNotebookExecutionJobRequest) returns (Operation) Deletes a NotebookExecutionJob. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.notebookExecutionJobs.delete For more information, see the IAM documentation.

DeleteNotebookRuntime

rpc DeleteNotebookRuntime(DeleteNotebookRuntimeRequest) returns (Operation) Deletes a NotebookRuntime. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.notebookRuntimes.delete For more information, see the IAM documentation.

DeleteNotebookRuntimeTemplate

rpc DeleteNotebookRuntimeTemplate(DeleteNotebookRuntimeTemplateRequest) returns (Operation) Deletes a NotebookRuntimeTemplate. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.notebookRuntimeTemplates.delete For more information, see the IAM documentation.

GetNotebookExecutionJob

rpc GetNotebookExecutionJob(GetNotebookExecutionJobRequest) returns (NotebookExecutionJob) Gets a NotebookExecutionJob. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.notebookExecutionJobs.get For more information, see the IAM documentation.

GetNotebookRuntime

rpc GetNotebookRuntime(GetNotebookRuntimeRequest) returns (NotebookRuntime) Gets a NotebookRuntime. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.notebookRuntimes.get For more information, see the IAM documentation.

GetNotebookRuntimeTemplate

rpc GetNotebookRuntimeTemplate(GetNotebookRuntimeTemplateRequest) returns (NotebookRuntimeTemplate) Gets a NotebookRuntimeTemplate. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.notebookRuntimeTemplates.get For more information, see the IAM documentation.

ListNotebookExecutionJobs

rpc ListNotebookExecutionJobs(ListNotebookExecutionJobsRequest) returns (ListNotebookExecutionJobsResponse) Lists NotebookExecutionJobs in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.notebookExecutionJobs.list For more information, see the IAM documentation.

ListNotebookRuntimeTemplates

rpc ListNotebookRuntimeTemplates(ListNotebookRuntimeTemplatesRequest) returns (ListNotebookRuntimeTemplatesResponse) Lists NotebookRuntimeTemplates in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.notebookRuntimeTemplates.list For more information, see the IAM documentation.

ListNotebookRuntimes

rpc ListNotebookRuntimes(ListNotebookRuntimesRequest) returns (ListNotebookRuntimesResponse) Lists NotebookRuntimes in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.notebookRuntimes.list For more information, see the IAM documentation.

StartNotebookRuntime

rpc StartNotebookRuntime(StartNotebookRuntimeRequest) returns (Operation) Starts a NotebookRuntime. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.notebookRuntimes.start For more information, see the IAM documentation.

StopNotebookRuntime
rpc StopNotebookRuntime(StopNotebookRuntimeRequest) returns (Operation) Stops a NotebookRuntime. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

UpdateNotebookRuntimeTemplate

rpc UpdateNotebookRuntimeTemplate(UpdateNotebookRuntimeTemplateRequest) returns (NotebookRuntimeTemplate) Updates a NotebookRuntimeTemplate. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.notebookRuntimeTemplates.update For more information, see the IAM documentation.

UpgradeNotebookRuntime

rpc UpgradeNotebookRuntime(UpgradeNotebookRuntimeRequest) returns (Operation) Upgrades a NotebookRuntime. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.notebookRuntimes.upgrade For more information, see the IAM documentation.

OnlineEvaluatorService

This service is used to create and manage Agent Platform OnlineEvaluators.

ActivateOnlineEvaluator

rpc ActivateOnlineEvaluator(ActivateOnlineEvaluatorRequest) returns (Operation) Activates an OnlineEvaluator. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.onlineEvaluators.update For more information, see the IAM documentation.

CreateOnlineEvaluator

rpc CreateOnlineEvaluator(CreateOnlineEvaluatorRequest) returns (Operation) Creates an OnlineEvaluator in the given project and location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.onlineEvaluators.create For more information, see the IAM documentation.

DeleteOnlineEvaluator

rpc DeleteOnlineEvaluator(DeleteOnlineEvaluatorRequest) returns (Operation) Deletes an OnlineEvaluator. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.onlineEvaluators.delete For more information, see the IAM documentation.

GetOnlineEvaluator

rpc GetOnlineEvaluator(GetOnlineEvaluatorRequest) returns (OnlineEvaluator) Gets details of an OnlineEvaluator. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.onlineEvaluators.get For more information, see the IAM documentation.

ListOnlineEvaluators

rpc ListOnlineEvaluators(ListOnlineEvaluatorsRequest) returns (ListOnlineEvaluatorsResponse) Lists the OnlineEvaluators for the given project and location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.onlineEvaluators.list For more information, see the IAM documentation.

SuspendOnlineEvaluator

rpc SuspendOnlineEvaluator(SuspendOnlineEvaluatorRequest) returns (Operation) Suspends an OnlineEvaluator. When an OnlineEvaluator is suspended, it won't run any evaluations until it is activated again. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.onlineEvaluators.update For more information, see the IAM documentation.

UpdateOnlineEvaluator

rpc UpdateOnlineEvaluator(UpdateOnlineEvaluatorRequest) returns (Operation) Updates the fields of an OnlineEvaluator. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.onlineEvaluators.update For more information, see the IAM documentation.

PersistentResourceService

A service for managing Agent Platform's machine learning PersistentResource.

CreatePersistentResource

rpc CreatePersistentResource(CreatePersistentResourceRequest) returns (Operation) Creates a PersistentResource. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.persistentResources.create For more information, see the IAM documentation.

DeletePersistentResource

rpc DeletePersistentResource(DeletePersistentResourceRequest) returns (Operation) Deletes a PersistentResource. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.persistentResources.delete For more information, see the IAM documentation.

GetPersistentResource

rpc GetPersistentResource(GetPersistentResourceRequest) returns (PersistentResource) Gets a PersistentResource. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.persistentResources.get For more information, see the IAM documentation.

ListPersistentResources

rpc ListPersistentResources(ListPersistentResourcesRequest) returns (ListPersistentResourcesResponse) Lists PersistentResources in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.persistentResources.list For more information, see the IAM documentation.

RebootPersistentResource
rpc RebootPersistentResource(RebootPersistentResourceRequest) returns (Operation) Reboots a PersistentResource. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

UpdatePersistentResource
rpc UpdatePersistentResource(UpdatePersistentResourceRequest) returns (Operation) Updates a PersistentResource. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

PipelineService

A service for creating and managing Agent Platform's pipelines. This includes both TrainingPipeline resources (used for AutoML and custom training) and PipelineJob resources (used for Agent Platform Pipelines).

BatchCancelPipelineJobs

rpc BatchCancelPipelineJobs(BatchCancelPipelineJobsRequest) returns (Operation) Batch cancel PipelineJobs. Firstly the server will check if all the jobs are in non-terminal states, and skip the jobs that are already terminated. If the operation failed, none of the pipeline jobs are cancelled. The server will poll the states of all the pipeline jobs periodically to check the cancellation status. This operation will return an LRO. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.pipelineJobs.cancel For more information, see the IAM documentation.

BatchDeletePipelineJobs

rpc BatchDeletePipelineJobs(BatchDeletePipelineJobsRequest) returns (Operation) Batch deletes PipelineJobs The Operation is atomic. If it fails, none of the PipelineJobs are deleted. If it succeeds, all of the PipelineJobs are deleted. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.pipelineJobs.delete For more information, see the IAM documentation.

CancelPipelineJob

rpc CancelPipelineJob(CancelPipelineJobRequest) returns (Empty) Cancels a PipelineJob. Starts asynchronous cancellation on the PipelineJob. The server makes a best effort to cancel the pipeline, but success is not guaranteed. Clients can use PipelineService.GetPipelineJob or other methods to check whether the cancellation succeeded or whether the pipeline completed despite cancellation. On successful cancellation, the PipelineJob is not deleted; instead it becomes a pipeline with a PipelineJob.error value with a google.rpc.Status.code of 1, corresponding to Code.CANCELLED, and PipelineJob.state is set to CANCELLED. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.pipelineJobs.cancel For more information, see the IAM documentation.

CancelTrainingPipeline

rpc CancelTrainingPipeline(CancelTrainingPipelineRequest) returns (Empty) Cancels a TrainingPipeline. Starts asynchronous cancellation on the TrainingPipeline. The server makes a best effort to cancel the pipeline, but success is not guaranteed. Clients can use PipelineService.GetTrainingPipeline or other methods to check whether the cancellation succeeded or whether the pipeline completed despite cancellation. On successful cancellation, the TrainingPipeline is not deleted; instead it becomes a pipeline with a TrainingPipeline.error value with a google.rpc.Status.code of 1, corresponding to Code.CANCELLED, and TrainingPipeline.state is set to CANCELLED. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.trainingPipelines.cancel For more information, see the IAM documentation.

CreatePipelineJob

rpc CreatePipelineJob(CreatePipelineJobRequest) returns (PipelineJob) Creates a PipelineJob. A PipelineJob will run immediately when created. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.pipelineJobs.create For more information, see the IAM documentation.

CreateTrainingPipeline

rpc CreateTrainingPipeline(CreateTrainingPipelineRequest) returns (TrainingPipeline) Creates a TrainingPipeline. A created TrainingPipeline right away will be attempted to be run. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.trainingPipelines.create For more information, see the IAM documentation.

DeletePipelineJob

rpc DeletePipelineJob(DeletePipelineJobRequest) returns (Operation) Deletes a PipelineJob. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.pipelineJobs.delete For more information, see the IAM documentation.

DeleteTrainingPipeline

rpc DeleteTrainingPipeline(DeleteTrainingPipelineRequest) returns (Operation) Deletes a TrainingPipeline. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.trainingPipelines.delete For more information, see the IAM documentation.

GetPipelineJob
rpc GetPipelineJob(GetPipelineJobRequest) returns (PipelineJob) Gets a PipelineJob. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.pipelineJobs.get For more information, see the IAM documentation.

GetTrainingPipeline

rpc GetTrainingPipeline(GetTrainingPipelineRequest) returns (TrainingPipeline) Gets a TrainingPipeline. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.trainingPipelines.get For more information, see the IAM documentation.

ListPipelineJobs

rpc ListPipelineJobs(ListPipelineJobsRequest) returns (ListPipelineJobsResponse) Lists PipelineJobs in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.pipelineJobs.list For more information, see the IAM documentation.

ListTrainingPipelines

rpc ListTrainingPipelines(ListTrainingPipelinesRequest) returns (ListTrainingPipelinesResponse) Lists TrainingPipelines in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.trainingPipelines.list For more information, see the IAM documentation.

PredictionService

A service for online predictions and explanations.

ChatCompletions

rpc ChatCompletions(ChatCompletionsRequest) returns (HttpBody) Exposes an OpenAI-compatible endpoint for chat completions. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/cloud-vertex-ai.firstparty.predict For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the endpoint resource: aiplatform.endpoints.predict For more information, see the IAM documentation.

CountTokens

rpc CountTokens(CountTokensRequest) returns (CountTokensResponse) Perform a token counting. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/cloud-vertex-ai.firstparty.predict For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the endpoint resource: aiplatform.endpoints.predict For more information, see the IAM documentation.

DeleteResponse
rpc DeleteResponse(DeleteResponseRequest) returns (HttpBody) Deletes the response from the endpoint. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/cloud-vertex-ai.firstparty.predict For more information, see the Authentication Overview.

DirectPredict

rpc DirectPredict(DirectPredictRequest) returns (DirectPredictResponse) Perform an unary online prediction request to a gRPC model server for Vertex first-party products and frameworks. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/cloud-vertex-ai.firstparty.predict For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the endpoint resource: aiplatform.endpoints.predict For more information, see the IAM documentation.

DirectRawPredict

rpc DirectRawPredict(DirectRawPredictRequest) returns (DirectRawPredictResponse) Perform an unary online prediction request to a gRPC model server for custom containers. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/cloud-vertex-ai.firstparty.predict For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the endpoint resource: aiplatform.endpoints.predict For more information, see the IAM documentation.

EmbedContent

rpc EmbedContent(EmbedContentRequest) returns (EmbedContentResponse) Embed content with multimodal inputs. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/cloud-vertex-ai.firstparty.predict For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the model resource: aiplatform.endpoints.predict For more information, see the IAM documentation.

Explain

rpc Explain(ExplainRequest) returns (ExplainResponse) Perform an online explanation. If deployed_model_id is specified, the corresponding DeployModel must have explanation_spec populated. If deployed_model_id is not specified, all DeployedModels must have explanation_spec populated. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/cloud-vertex-ai.firstparty.predict For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the endpoint resource: aiplatform.endpoints.explain For more information, see the IAM documentation.

GenerateContent

rpc GenerateContent(GenerateContentRequest) returns (GenerateContentResponse) Generate content with multimodal inputs. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/cloud-vertex-ai.firstparty.predict For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the model resource: aiplatform.endpoints.predict For more information, see the IAM documentation.

GetResponse
rpc GetResponse(GetResponseRequest) returns (HttpBody) Gets the response from the endpoint. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/cloud-vertex-ai.firstparty.predict For more information, see the Authentication Overview.

Predict

rpc Predict(PredictRequest) returns (PredictResponse) Perform an online inference. Use this method to run inference on Google's generative AI models as well as custom models deployed to Gemini Enterprise Agent Platform. This method supports both generative AI tasks (such as image generation, virtual try-on, text generation, and multimodal embeddings) and traditional machine learning tasks (such as classification and regression). To run inference on a base (non-tuned) Gemini model, see GenerateContent. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/cloud-vertex-ai.firstparty.predict For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the endpoint resource: aiplatform.endpoints.predict For more information, see the IAM documentation.

PredictLongRunning

rpc PredictLongRunning(PredictLongRunningRequest) returns (Operation) Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/cloud-vertex-ai.firstparty.predict For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the endpoint resource: aiplatform.endpoints.predict For more information, see the IAM documentation.

RawPredict

rpc RawPredict(RawPredictRequest) returns (HttpBody) Perform an online prediction with an arbitrary HTTP payload. The response includes the following HTTP headers: X-Vertex-AI-Endpoint-Id: ID of the Endpoint that served this prediction. X-Vertex-AI-Deployed-Model-Id: ID of the Endpoint's DeployedModel that served this prediction. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/cloud-vertex-ai.firstparty.predict For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the endpoint resource: aiplatform.endpoints.predict For more information, see the IAM documentation.

ServerStreamingPredict

rpc ServerStreamingPredict(StreamingPredictRequest) returns (StreamingPredictResponse) Perform a server-side streaming online prediction request for Vertex LLM streaming. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/cloud-vertex-ai.firstparty.predict For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the endpoint resource: aiplatform.endpoints.predict For more information, see the IAM documentation.

StreamDirectPredict

rpc StreamDirectPredict(StreamDirectPredictRequest) returns (StreamDirectPredictResponse) Perform a streaming online prediction request to a gRPC model server for Vertex first-party products and frameworks. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/cloud-vertex-ai.firstparty.predict For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the endpoint resource: aiplatform.endpoints.predict For more information, see the IAM documentation.

StreamDirectRawPredict

rpc StreamDirectRawPredict(StreamDirectRawPredictRequest) returns (StreamDirectRawPredictResponse) Perform a streaming online prediction request to a gRPC model server for custom containers. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/cloud-vertex-ai.firstparty.predict For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the endpoint resource: aiplatform.endpoints.predict For more information, see the IAM documentation.

StreamGenerateContent

rpc StreamGenerateContent(GenerateContentRequest) returns (GenerateContentResponse) Generate content with multimodal inputs with streaming support. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/cloud-vertex-ai.firstparty.predict For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the model resource: aiplatform.endpoints.predict For more information, see the IAM documentation.

StreamRawPredict

rpc StreamRawPredict(StreamRawPredictRequest) returns (HttpBody) Perform a streaming online prediction with an arbitrary HTTP payload. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/cloud-vertex-ai.firstparty.predict For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the endpoint resource: aiplatform.endpoints.predict For more information, see the IAM documentation.

StreamingPredict

rpc StreamingPredict(StreamingPredictRequest) returns (StreamingPredictResponse) Perform a streaming online prediction request for Vertex first-party products and frameworks. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/cloud-vertex-ai.firstparty.predict For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the endpoint resource: aiplatform.endpoints.predict For more information, see the IAM documentation.

StreamingRawPredict

rpc StreamingRawPredict(StreamingRawPredictRequest) returns (StreamingRawPredictResponse) Perform a streaming online prediction request through gRPC. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only https://www.googleapis.com/auth/cloud-vertex-ai.firstparty.predict For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the endpoint resource: aiplatform.endpoints.predict For more information, see the IAM documentation.

ReasoningEngineExecutionService

A service for executing queries on Reasoning Engine.

AsyncQueryReasoningEngine

rpc AsyncQueryReasoningEngine(AsyncQueryReasoningEngineRequest) returns (Operation) Async query using a reasoning engine. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.reasoningEngines.query For more information, see the IAM documentation.

BidiInvokeReasoningEngine

rpc BidiInvokeReasoningEngine(BidiInvokeReasoningEngineRequest) returns (HttpBody) Invokes reasoning engine with arbitrary WebSocket requests for bidi streaming Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires one of the following IAM permissions on the name resource, depending on the resource type: aiplatform.reasoningEngines.query aiplatform.reasoningEngineRuntimeRevisions.query For more information, see the IAM documentation.

CancelAsyncQueryReasoningEngine

rpc CancelAsyncQueryReasoningEngine(CancelAsyncQueryReasoningEngineRequest) returns (CancelAsyncQueryReasoningEngineResponse) Cancels an AsyncQueryReasoningEngine operation. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.reasoningEngines.query For more information, see the IAM documentation.

InvokeReasoningEngine

rpc InvokeReasoningEngine(InvokeReasoningEngineRequest) returns (HttpBody) Invokes reasoning engine with arbitrary HTTP requests for both unary and server-side streaming cases. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires one of the following IAM permissions on the name resource, depending on the resource type: aiplatform.reasoningEngines.query aiplatform.reasoningEngineRuntimeRevisions.query For more information, see the IAM documentation.

QueryReasoningEngine

rpc QueryReasoningEngine(QueryReasoningEngineRequest) returns (QueryReasoningEngineResponse) Queries using a reasoning engine. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires one of the following IAM permissions on the name resource, depending on the resource type: aiplatform.reasoningEngines.query aiplatform.reasoningEngineRuntimeRevisions.query For more information, see the IAM documentation.

StreamQueryReasoningEngine

rpc StreamQueryReasoningEngine(StreamQueryReasoningEngineRequest) returns (HttpBody) Streams queries using a reasoning engine. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires one of the following IAM permissions on the name resource, depending on the resource type: aiplatform.reasoningEngines.query aiplatform.reasoningEngineRuntimeRevisions.query For more information, see the IAM documentation.

ReasoningEngineRuntimeRevisionService

Manages Agent Platform's Reasoning Engine Revisions.

DeleteReasoningEngineRuntimeRevision

rpc DeleteReasoningEngineRuntimeRevision(DeleteReasoningEngineRuntimeRevisionRequest) returns (Operation) Deletes a reasoning engine revision. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.reasoningEngineRuntimeRevisions.delete For more information, see the IAM documentation.

GetReasoningEngineRuntimeRevision

rpc GetReasoningEngineRuntimeRevision(GetReasoningEngineRuntimeRevisionRequest) returns (ReasoningEngineRuntimeRevision) Gets a reasoning engine runtime revision. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.reasoningEngineRuntimeRevisions.get For more information, see the IAM documentation.

ListReasoningEngineRuntimeRevisions

rpc ListReasoningEngineRuntimeRevisions(ListReasoningEngineRuntimeRevisionsRequest) returns (ListReasoningEngineRuntimeRevisionsResponse) Lists runtime revisions in a reasoning engine. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.reasoningEngineRuntimeRevisions.list For more information, see the IAM documentation.

ReasoningEngineService

A service for managing Agent Platform's Reasoning Engines.

CreateReasoningEngine

rpc CreateReasoningEngine(CreateReasoningEngineRequest) returns (Operation) Creates a reasoning engine. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.reasoningEngines.create For more information, see the IAM documentation.

DeleteReasoningEngine

rpc DeleteReasoningEngine(DeleteReasoningEngineRequest) returns (Operation) Deletes a reasoning engine. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.reasoningEngines.delete For more information, see the IAM documentation.

GetReasoningEngine

rpc GetReasoningEngine(GetReasoningEngineRequest) returns (ReasoningEngine) Gets a reasoning engine. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.reasoningEngines.get For more information, see the IAM documentation.

ListReasoningEngines

rpc ListReasoningEngines(ListReasoningEnginesRequest) returns (ListReasoningEnginesResponse) Lists reasoning engines in a location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.reasoningEngines.list For more information, see the IAM documentation.

UpdateReasoningEngine

rpc UpdateReasoningEngine(UpdateReasoningEngineRequest) returns (Operation) Updates a reasoning engine. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.reasoningEngines.update For more information, see the IAM documentation.

ScheduleService

A service for creating and managing Agent Platform's Schedule resources to periodically launch shceudled runs to make API calls.

CreateSchedule

rpc CreateSchedule(CreateScheduleRequest) returns (Schedule) Creates a Schedule. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permissions on the parent resource: aiplatform.pipelineJobs.create aiplatform.schedules.create For more information, see the IAM documentation.

DeleteSchedule
rpc DeleteSchedule(DeleteScheduleRequest) returns (Operation) Deletes a Schedule. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.schedules.delete For more information, see the IAM documentation.

GetSchedule
rpc GetSchedule(GetScheduleRequest) returns (Schedule) Gets a Schedule. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.schedules.get For more information, see the IAM documentation.

ListSchedules

rpc ListSchedules(ListSchedulesRequest) returns (ListSchedulesResponse) Lists Schedules in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.schedules.list For more information, see the IAM documentation.

PauseSchedule

rpc PauseSchedule(PauseScheduleRequest) returns (Empty) Pauses a Schedule. Will mark Schedule.state to 'PAUSED'. If the schedule is paused, no new runs will be created. Already created runs will NOT be paused or canceled. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.schedules.update For more information, see the IAM documentation.

ResumeSchedule

rpc ResumeSchedule(ResumeScheduleRequest) returns (Empty) Resumes a paused Schedule to start scheduling new runs. Will mark Schedule.state to 'ACTIVE'. Only paused Schedule can be resumed. When the Schedule is resumed, new runs will be scheduled starting from the next execution time after the current time based on the time_specification in the Schedule. If Schedule.catch_up is set up true, all missed runs will be scheduled for backfill first. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.schedules.update For more information, see the IAM documentation.

UpdateSchedule

rpc UpdateSchedule(UpdateScheduleRequest) returns (Schedule) Updates an active or paused Schedule. When the Schedule is updated, new runs will be scheduled starting from the updated next execution time after the update time based on the time_specification in the updated Schedule. All unstarted runs before the update time will be skipped while already created runs will NOT be paused or canceled. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.schedules.update For more information, see the IAM documentation.

SessionService

The service that manages Vertex Session related resources.

AppendEvent
rpc AppendEvent(AppendEventRequest) returns (AppendEventResponse) Appends an event to a given session. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

CreateSession
rpc CreateSession(CreateSessionRequest) returns (Operation) Creates a new Session. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

DeleteSession
rpc DeleteSession(DeleteSessionRequest) returns (Operation) Deletes details of the specific Session. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

GetSession
rpc GetSession(GetSessionRequest) returns (Session) Gets details of the specific Session. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

ListEvents
rpc ListEvents(ListEventsRequest) returns (ListEventsResponse) Lists Events in a given session. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

ListSessions

rpc ListSessions(ListSessionsRequest) returns (ListSessionsResponse) Lists Sessions in a given reasoning engine. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.sessions.list For more information, see the IAM documentation.

UpdateSession
rpc UpdateSession(UpdateSessionRequest) returns (Session) Updates the specific Session. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

SkillRegistryService

A service for managing skills for LLM applications.

CreateSkill
rpc CreateSkill(CreateSkillRequest) returns (Operation) Create a Skill. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

DeleteSkill
rpc DeleteSkill(DeleteSkillRequest) returns (Operation) Delete a Skill. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

GetSkill
rpc GetSkill(GetSkillRequest) returns (Skill) Get a Skill. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

GetSkillRevision
rpc GetSkillRevision(GetSkillRevisionRequest) returns (SkillRevision) Get a Skill Revision. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

ListSkillRevisions
rpc ListSkillRevisions(ListSkillRevisionsRequest) returns (ListSkillRevisionsResponse) List Skill Revisions for a Skill. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

ListSkills
rpc ListSkills(ListSkillsRequest) returns (ListSkillsResponse) List Skills. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

RetrieveSkills
rpc RetrieveSkills(RetrieveSkillsRequest) returns (RetrieveSkillsResponse) Retrieves skills. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

UpdateSkill
rpc UpdateSkill(UpdateSkillRequest) returns (Operation) Update a Skill. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

SpecialistPoolService

A service for creating and managing Customer SpecialistPools. When customers start Data Labeling jobs, they can reuse/create Specialist Pools to bring their own Specialists to label the data. Customers can add/remove Managers for the Specialist Pool on Cloud console, then Managers will get email notifications to manage Specialists and tasks on CrowdCompute console.

CreateSpecialistPool

rpc CreateSpecialistPool(CreateSpecialistPoolRequest) returns (Operation) Creates a SpecialistPool. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.specialistPools.create For more information, see the IAM documentation.

DeleteSpecialistPool

rpc DeleteSpecialistPool(DeleteSpecialistPoolRequest) returns (Operation) Deletes a SpecialistPool as well as all Specialists in the pool. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.specialistPools.delete For more information, see the IAM documentation.

GetSpecialistPool

rpc GetSpecialistPool(GetSpecialistPoolRequest) returns (SpecialistPool) Gets a SpecialistPool. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.specialistPools.get For more information, see the IAM documentation.

ListSpecialistPools

rpc ListSpecialistPools(ListSpecialistPoolsRequest) returns (ListSpecialistPoolsResponse) Lists SpecialistPools in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.specialistPools.list For more information, see the IAM documentation.

UpdateSpecialistPool

rpc UpdateSpecialistPool(UpdateSpecialistPoolRequest) returns (Operation) Updates a SpecialistPool. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.specialistPools.update For more information, see the IAM documentation.

TensorboardService

TensorboardService

BatchCreateTensorboardRuns

rpc BatchCreateTensorboardRuns(BatchCreateTensorboardRunsRequest) returns (BatchCreateTensorboardRunsResponse) Batch create TensorboardRuns. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.tensorboardRuns.batchCreate For more information, see the IAM documentation.

BatchCreateTensorboardTimeSeries

rpc BatchCreateTensorboardTimeSeries(BatchCreateTensorboardTimeSeriesRequest) returns (BatchCreateTensorboardTimeSeriesResponse) Batch create TensorboardTimeSeries that belong to a TensorboardExperiment. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.tensorboardTimeSeries.batchCreate For more information, see the IAM documentation.

BatchReadTensorboardTimeSeriesData

rpc BatchReadTensorboardTimeSeriesData(BatchReadTensorboardTimeSeriesDataRequest) returns (BatchReadTensorboardTimeSeriesDataResponse) Reads multiple TensorboardTimeSeries' data. The data point number limit is 1000 for scalars, 100 for tensors and blob references. If the number of data points stored is less than the limit, all data is returned. Otherwise, the number limit of data points is randomly selected from this time series and returned. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the tensorboard resource: aiplatform.tensorboardTimeSeries.batchRead For more information, see the IAM documentation.

CreateTensorboard

rpc CreateTensorboard(CreateTensorboardRequest) returns (Operation) Creates a Tensorboard. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.tensorboards.create For more information, see the IAM documentation.

CreateTensorboardExperiment

rpc CreateTensorboardExperiment(CreateTensorboardExperimentRequest) returns (TensorboardExperiment) Creates a TensorboardExperiment. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.tensorboardExperiments.create For more information, see the IAM documentation.

CreateTensorboardRun

rpc CreateTensorboardRun(CreateTensorboardRunRequest) returns (TensorboardRun) Creates a TensorboardRun. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.tensorboardRuns.create For more information, see the IAM documentation.

CreateTensorboardTimeSeries

rpc CreateTensorboardTimeSeries(CreateTensorboardTimeSeriesRequest) returns (TensorboardTimeSeries) Creates a TensorboardTimeSeries. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.tensorboardTimeSeries.create For more information, see the IAM documentation.

DeleteTensorboard

rpc DeleteTensorboard(DeleteTensorboardRequest) returns (Operation) Deletes a Tensorboard. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.tensorboards.delete For more information, see the IAM documentation.

DeleteTensorboardExperiment

rpc DeleteTensorboardExperiment(DeleteTensorboardExperimentRequest) returns (Operation) Deletes a TensorboardExperiment. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.tensorboardExperiments.delete For more information, see the IAM documentation.

DeleteTensorboardRun

rpc DeleteTensorboardRun(DeleteTensorboardRunRequest) returns (Operation) Deletes a TensorboardRun. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.tensorboardRuns.delete For more information, see the IAM documentation.

DeleteTensorboardTimeSeries

rpc DeleteTensorboardTimeSeries(DeleteTensorboardTimeSeriesRequest) returns (Operation) Deletes a TensorboardTimeSeries. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.tensorboardTimeSeries.delete For more information, see the IAM documentation.

ExportTensorboardTimeSeriesData

rpc ExportTensorboardTimeSeriesData(ExportTensorboardTimeSeriesDataRequest) returns (ExportTensorboardTimeSeriesDataResponse) Exports a TensorboardTimeSeries' data. Data is returned in paginated responses. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the tensorboardTimeSeries resource: aiplatform.tensorboardTimeSeries.read For more information, see the IAM documentation.

GetTensorboard

rpc GetTensorboard(GetTensorboardRequest) returns (Tensorboard) Gets a Tensorboard. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.tensorboards.get For more information, see the IAM documentation.

GetTensorboardExperiment

rpc GetTensorboardExperiment(GetTensorboardExperimentRequest) returns (TensorboardExperiment) Gets a TensorboardExperiment. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.tensorboardExperiments.get For more information, see the IAM documentation.

GetTensorboardRun

rpc GetTensorboardRun(GetTensorboardRunRequest) returns (TensorboardRun) Gets a TensorboardRun. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.tensorboardRuns.get For more information, see the IAM documentation.

GetTensorboardTimeSeries

rpc GetTensorboardTimeSeries(GetTensorboardTimeSeriesRequest) returns (TensorboardTimeSeries) Gets a TensorboardTimeSeries. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.tensorboardTimeSeries.get For more information, see the IAM documentation.

ListTensorboardExperiments

rpc ListTensorboardExperiments(ListTensorboardExperimentsRequest) returns (ListTensorboardExperimentsResponse) Lists TensorboardExperiments in a Location. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.tensorboardExperiments.list For more information, see the IAM documentation.

ListTensorboardRuns

rpc ListTensorboardRuns(ListTensorboardRunsRequest) returns (ListTensorboardRunsResponse) Lists TensorboardRuns in a Location. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.tensorboardRuns.list For more information, see the IAM documentation.

ListTensorboardTimeSeries

rpc ListTensorboardTimeSeries(ListTensorboardTimeSeriesRequest) returns (ListTensorboardTimeSeriesResponse) Lists TensorboardTimeSeries in a Location. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.tensorboardTimeSeries.list For more information, see the IAM documentation.

ListTensorboards

rpc ListTensorboards(ListTensorboardsRequest) returns (ListTensorboardsResponse) Lists Tensorboards in a Location. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.tensorboards.list For more information, see the IAM documentation.

ReadTensorboardBlobData

rpc ReadTensorboardBlobData(ReadTensorboardBlobDataRequest) returns (ReadTensorboardBlobDataResponse) Gets bytes of TensorboardBlobs. This is to allow reading blob data stored in consumer project's Cloud Storage bucket without users having to obtain Cloud Storage access permission. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the timeSeries resource: aiplatform.tensorboardTimeSeries.read For more information, see the IAM documentation.

ReadTensorboardSize
rpc ReadTensorboardSize(ReadTensorboardSizeRequest) returns (ReadTensorboardSizeResponse) Returns the storage size for a given TensorBoard instance. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

ReadTensorboardTimeSeriesData

rpc ReadTensorboardTimeSeriesData(ReadTensorboardTimeSeriesDataRequest) returns (ReadTensorboardTimeSeriesDataResponse) Reads a TensorboardTimeSeries' data. By default, if the number of data points stored is less than 1000, all data is returned. Otherwise, 1000 data points is randomly selected from this time series and returned. This value can be changed by changing max_data_points, which can't be greater than 10k. Authorization scopes Requires one of the following OAuth scopes: https://www.googleapis.com/auth/cloud-platform https://www.googleapis.com/auth/cloud-platform.read-only For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the tensorboardTimeSeries resource: aiplatform.tensorboardTimeSeries.read For more information, see the IAM documentation.

ReadTensorboardUsage
rpc ReadTensorboardUsage(ReadTensorboardUsageRequest) returns (ReadTensorboardUsageResponse) Returns a list of monthly active users for a given TensorBoard instance. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

UpdateTensorboard

rpc UpdateTensorboard(UpdateTensorboardRequest) returns (Operation) Updates a Tensorboard. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.tensorboards.update For more information, see the IAM documentation.

UpdateTensorboardExperiment

rpc UpdateTensorboardExperiment(UpdateTensorboardExperimentRequest) returns (TensorboardExperiment) Updates a TensorboardExperiment. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.tensorboardExperiments.update For more information, see the IAM documentation.

UpdateTensorboardRun

rpc UpdateTensorboardRun(UpdateTensorboardRunRequest) returns (TensorboardRun) Updates a TensorboardRun. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.tensorboardRuns.update For more information, see the IAM documentation.

UpdateTensorboardTimeSeries

rpc UpdateTensorboardTimeSeries(UpdateTensorboardTimeSeriesRequest) returns (TensorboardTimeSeries) Updates a TensorboardTimeSeries. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.tensorboardTimeSeries.update For more information, see the IAM documentation.

WriteTensorboardExperimentData

rpc WriteTensorboardExperimentData(WriteTensorboardExperimentDataRequest) returns (WriteTensorboardExperimentDataResponse) Write time series data points of multiple TensorboardTimeSeries in multiple TensorboardRun's. If any data fail to be ingested, an error is returned. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the tensorboardExperiment resource: aiplatform.tensorboardExperiments.write For more information, see the IAM documentation.

WriteTensorboardRunData

rpc WriteTensorboardRunData(WriteTensorboardRunDataRequest) returns (WriteTensorboardRunDataResponse) Write time series data points into multiple TensorboardTimeSeries under a TensorboardRun. If any data fail to be ingested, an error is returned. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the tensorboardRun resource: aiplatform.tensorboardRuns.write For more information, see the IAM documentation.

VertexRagDataService

A service for managing user data for RAG.

BatchCreateRagDataSchemas
rpc BatchCreateRagDataSchemas(BatchCreateRagDataSchemasRequest) returns (Operation) Batch Create one or more RagDataSchemas Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

BatchCreateRagMetadata
rpc BatchCreateRagMetadata(BatchCreateRagMetadataRequest) returns (Operation) Batch Create one or more RagMetadatas Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

BatchDeleteRagDataSchemas
rpc BatchDeleteRagDataSchemas(BatchDeleteRagDataSchemasRequest) returns (Operation) Batch Deletes one or more RagDataSchemas Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

BatchDeleteRagMetadata
rpc BatchDeleteRagMetadata(BatchDeleteRagMetadataRequest) returns (Operation) Batch Deletes one or more RagMetadata. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

CreateRagCorpus
rpc CreateRagCorpus(CreateRagCorpusRequest) returns (Operation) Creates a RagCorpus. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.ragCorpora.create For more information, see the IAM documentation.

CreateRagDataSchema
rpc CreateRagDataSchema(CreateRagDataSchemaRequest) returns (RagDataSchema) Creates a RagDataSchema. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

CreateRagMetadata
rpc CreateRagMetadata(CreateRagMetadataRequest) returns (RagMetadata) Creates a RagMetadata. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

DeleteRagCorpus
rpc DeleteRagCorpus(DeleteRagCorpusRequest) returns (Operation) Deletes a RagCorpus. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.ragCorpora.delete For more information, see the IAM documentation.

DeleteRagDataSchema
rpc DeleteRagDataSchema(DeleteRagDataSchemaRequest) returns (Empty) Deletes a RagDataSchema. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

DeleteRagFile
rpc DeleteRagFile(DeleteRagFileRequest) returns (Operation) Deletes a RagFile. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.ragFiles.delete For more information, see the IAM documentation.

DeleteRagMetadata
rpc DeleteRagMetadata(DeleteRagMetadataRequest) returns (Empty) Deletes a RagMetadata. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

GetRagCorpus
rpc GetRagCorpus(GetRagCorpusRequest) returns (RagCorpus) Gets a RagCorpus. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.ragCorpora.get For more information, see the IAM documentation.

GetRagDataSchema
rpc GetRagDataSchema(GetRagDataSchemaRequest) returns (RagDataSchema) Gets a RagDataSchema. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

GetRagEngineConfig

rpc GetRagEngineConfig(GetRagEngineConfigRequest) returns (RagEngineConfig) Gets a RagEngineConfig. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.ragEngineConfigs.get For more information, see the IAM documentation.

GetRagFile
rpc GetRagFile(GetRagFileRequest) returns (RagFile) Gets a RagFile. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.ragFiles.get For more information, see the IAM documentation.

GetRagMetadata
rpc GetRagMetadata(GetRagMetadataRequest) returns (RagMetadata) Gets a RagMetadata. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

ImportRagFiles

rpc ImportRagFiles(ImportRagFilesRequest) returns (Operation) Import files from Google Cloud Storage or Google Drive into a RagCorpus. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.ragFiles.import For more information, see the IAM documentation.

ListRagCorpora

rpc ListRagCorpora(ListRagCorporaRequest) returns (ListRagCorporaResponse) Lists RagCorpora in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.ragCorpora.list For more information, see the IAM documentation.

ListRagDataSchemas
rpc ListRagDataSchemas(ListRagDataSchemasRequest) returns (ListRagDataSchemasResponse) Lists RagDataSchemas in a Location. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

ListRagFiles

rpc ListRagFiles(ListRagFilesRequest) returns (ListRagFilesResponse) Lists RagFiles in a RagCorpus. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.ragFiles.list For more information, see the IAM documentation.

ListRagMetadata
rpc ListRagMetadata(ListRagMetadataRequest) returns (ListRagMetadataResponse) Lists RagMetadata in a RagFile. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

UpdateRagCorpus
rpc UpdateRagCorpus(UpdateRagCorpusRequest) returns (Operation) Updates a RagCorpus. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.ragCorpora.update For more information, see the IAM documentation.

UpdateRagEngineConfig

rpc UpdateRagEngineConfig(UpdateRagEngineConfigRequest) returns (Operation) Updates a RagEngineConfig. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.ragEngineConfigs.update For more information, see the IAM documentation.

UpdateRagMetadata
rpc UpdateRagMetadata(UpdateRagMetadataRequest) returns (RagMetadata) Updates a RagMetadata. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview.

VertexRagService

A service for retrieving relevant contexts.

AskContexts

rpc AskContexts(AskContextsRequest) returns (AskContextsResponse) Agentic Retrieval Ask API for RAG. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.ragCorpora.list For more information, see the IAM documentation.

AsyncRetrieveContexts

rpc AsyncRetrieveContexts(AsyncRetrieveContextsRequest) returns (Operation) Asynchronous API to retrieves relevant contexts for a query. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.ragCorpora.list For more information, see the IAM documentation.

AugmentPrompt

rpc AugmentPrompt(AugmentPromptRequest) returns (AugmentPromptResponse) Given an input prompt, it returns augmented prompt from vertex rag store to guide LLM towards generating grounded responses. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.ragCorpora.get For more information, see the IAM documentation.

CorroborateContent

rpc CorroborateContent(CorroborateContentRequest) returns (CorroborateContentResponse) Given an input text, it returns a score that evaluates the factuality of the text. It also extracts and returns claims from the text and provides supporting facts. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.ragCorpora.get For more information, see the IAM documentation.

RetrieveContexts

rpc RetrieveContexts(RetrieveContextsRequest) returns (RetrieveContextsResponse) Retrieves relevant contexts for a query. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.ragCorpora.get For more information, see the IAM documentation.

VizierService

Agent Platform Vizier API.

Agent Platform Vizier is a service to solve blackbox optimization problems, such as tuning machine learning hyperparameters and searching over deep learning architectures.

AddTrialMeasurement

rpc AddTrialMeasurement(AddTrialMeasurementRequest) returns (Trial) Adds a measurement of the objective metrics to a Trial. This measurement is assumed to have been taken before the Trial is complete. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the trialName resource: aiplatform.trials.update For more information, see the IAM documentation.

CheckTrialEarlyStoppingState

rpc CheckTrialEarlyStoppingState(CheckTrialEarlyStoppingStateRequest) returns (Operation) Checks whether a Trial should stop or not. Returns a long-running operation. When the operation is successful, it will contain a CheckTrialEarlyStoppingStateResponse. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the trialName resource: aiplatform.trials.get For more information, see the IAM documentation.

CompleteTrial
rpc CompleteTrial(CompleteTrialRequest) returns (Trial) Marks a Trial as complete. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.trials.update For more information, see the IAM documentation.

CreateStudy

rpc CreateStudy(CreateStudyRequest) returns (Study) Creates a Study. A resource name will be generated after creation of the Study. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.studies.create For more information, see the IAM documentation.

CreateTrial

rpc CreateTrial(CreateTrialRequest) returns (Trial) Adds a user provided Trial to a Study. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.trials.create For more information, see the IAM documentation.

DeleteStudy
rpc DeleteStudy(DeleteStudyRequest) returns (Empty) Deletes a Study. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.studies.delete For more information, see the IAM documentation.

DeleteTrial
rpc DeleteTrial(DeleteTrialRequest) returns (Empty) Deletes a Trial. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.trials.delete For more information, see the IAM documentation.

GetStudy
rpc GetStudy(GetStudyRequest) returns (Study) Gets a Study by name. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.studies.get For more information, see the IAM documentation.

GetTrial
rpc GetTrial(GetTrialRequest) returns (Trial) Gets a Trial. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.trials.get For more information, see the IAM documentation.

ListOptimalTrials

rpc ListOptimalTrials(ListOptimalTrialsRequest) returns (ListOptimalTrialsResponse) Lists the pareto-optimal Trials for multi-objective Study or the optimal Trials for single-objective Study. The definition of pareto-optimal can be checked in wiki page. https://en.wikipedia.org/wiki/Pareto_efficiency Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.trials.list For more information, see the IAM documentation.

ListStudies

rpc ListStudies(ListStudiesRequest) returns (ListStudiesResponse) Lists all the studies in a region for an associated project. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.studies.list For more information, see the IAM documentation.

ListTrials

rpc ListTrials(ListTrialsRequest) returns (ListTrialsResponse) Lists the Trials associated with a Study. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.trials.list For more information, see the IAM documentation.

LookupStudy

rpc LookupStudy(LookupStudyRequest) returns (Study) Looks a study up using the user-defined display_name field instead of the fully qualified resource name. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.studies.list For more information, see the IAM documentation.

StopTrial
rpc StopTrial(StopTrialRequest) returns (Trial) Stops a Trial. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the name resource: aiplatform.trials.update For more information, see the IAM documentation.

SuggestTrials

rpc SuggestTrials(SuggestTrialsRequest) returns (Operation) Adds one or more Trials to a Study, with parameter values suggested by Agent Platform Vizier. Returns a long-running operation associated with the generation of Trial suggestions. When this long-running operation succeeds, it will contain a SuggestTrialsResponse. Authorization scopes Requires the following OAuth scope: https://www.googleapis.com/auth/cloud-platform For more information, see the Authentication Overview. IAM Permissions Requires the following IAM permission on the parent resource: aiplatform.trials.create For more information, see the IAM documentation.

AcceleratorType

Represents a hardware accelerator type.

Enums
ACCELERATOR_TYPE_UNSPECIFIED	Unspecified accelerator type, which means no accelerator.
NVIDIA_TESLA_K80	Deprecated: Nvidia Tesla K80 GPU has reached end of support, see https://cloud.google.com/compute/docs/eol/k80-eol. This item is deprecated!
NVIDIA_TESLA_P100	Nvidia Tesla P100 GPU.
NVIDIA_TESLA_V100	Nvidia Tesla V100 GPU.
NVIDIA_TESLA_P4	Nvidia Tesla P4 GPU.
NVIDIA_TESLA_T4	Nvidia Tesla T4 GPU.
NVIDIA_TESLA_A100	Nvidia Tesla A100 GPU.
NVIDIA_A100_80GB	Nvidia A100 80GB GPU.
NVIDIA_L4	Nvidia L4 GPU.
NVIDIA_H100_80GB	Nvidia H100 80Gb GPU.
NVIDIA_H100_MEGA_80GB	Nvidia H100 Mega 80Gb GPU.
NVIDIA_H200_141GB	Nvidia H200 141Gb GPU.
NVIDIA_B200	Nvidia B200 GPU.
NVIDIA_GB200	Nvidia GB200 GPU.
NVIDIA_RTX_PRO_6000	Nvidia RTX Pro 6000 GPU.
TPU_V2	TPU v2.
TPU_V3	TPU v3.
TPU_V4_POD	TPU v4.
TPU_V5_LITEPOD	TPU v5.

AcceptPublisherModelEulaRequest

Request message for ModelGardenService.AcceptPublisherModelEula.

Fields
parent	string Required. The project requesting access for named model. The format is projects/{project}.
publisher_model	string Required. The name of the PublisherModel resource. Format: publishers/{publisher}/models/{publisher_model}, or publishers/hf-{hugging-face-author}/models/{hugging-face-model-name}

ActivateOnlineEvaluatorOperationMetadata

Metadata for the ActivateOnlineEvaluator operation.

Fields
generic_metadata	GenericOperationMetadata Common part of operation metadata.

ActivateOnlineEvaluatorRequest

Request message for ActivateOnlineEvaluator.

Fields
name	string Required. The name of the OnlineEvaluator to activate. Format: projects/{project}/locations/{location}/onlineEvaluators/{id}.

AddContextArtifactsAndExecutionsRequest

Request message for MetadataService.AddContextArtifactsAndExecutions.

Fields
context	string Required. The resource name of the Context that the Artifacts and Executions belong to. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/contexts/{context}
artifacts[]	string The resource names of the Artifacts to attribute to the Context. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact}
executions[]	string The resource names of the Executions to associate with the Context. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution}

AddContextArtifactsAndExecutionsResponse

This type has no fields.

Response message for MetadataService.AddContextArtifactsAndExecutions.

AddContextChildrenRequest

Request message for MetadataService.AddContextChildren.

Fields
context	string Required. The resource name of the parent Context. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/contexts/{context}
child_contexts[]	string The resource names of the child Contexts.

AddContextChildrenResponse

This type has no fields.

Response message for MetadataService.AddContextChildren.

AddExecutionEventsRequest

Request message for MetadataService.AddExecutionEvents.

Fields
execution	string Required. The resource name of the Execution that the Events connect Artifacts with. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution}
events[]	Event The Events to create and add.

AddExecutionEventsResponse

This type has no fields.

Response message for MetadataService.AddExecutionEvents.

AddTrialMeasurementRequest

Request message for VizierService.AddTrialMeasurement.

Fields
trial_name	string Required. The name of the trial to add measurement. Format: projects/{project}/locations/{location}/studies/{study}/trials/{trial}
measurement	Measurement Required. The measurement to be added to a Trial.

Agent

A Vertex agent contains instructions and configurations for the LLM to execute a certain task.

Fields
name	string Identifier. The resource name of the agent. Format: projects/{project}/locations/{location}/agents/{agent}.
id	string Immutable. The user-specified ID for the agent. This ID becomes the final component of the agent resource name. If not provided, Agent Platform will generate a value for this ID. The ID can be up to 63 characters and must match the regular expression [a-z]([a-z0-9-]{0,61}[a-z0-9])?.
created	Timestamp Output only. The time the agent was created.
updated	Timestamp Output only. The time the agent was last updated.
object	string Output only. The object type of the resource. For agents, the value is agent.
base_agent	string Required. The base agent for the agent. Supported values: * antigravity-preview-05-2026
metadata	map<string, string> Optional. The metadata for the agent.
description	string Optional. The description of the agent.
system_instruction	string Optional. The instructions for the agent to follow. These instructions are passed to the LLM as a system instruction.
tools[]	AgentTool Optional. The tools available to the agent.
Union field environment. The environment configuration for the agent. environment can be only one of the following:
base_environment	Value Optional. The base environment configuration for the agent. Valid types: A string value for the environment ID, or remote for the default. A struct value for the environment_config.

AgentConfig

Represents configuration for an Agent.

Fields
agent_type	string Optional. The type or class of the agent (e.g., "LlmAgent", "RouterAgent", "ToolUseAgent"). Useful for the autorater to understand the expected behavior of the agent.
description	string Optional. A high-level description of the agent's role and responsibilities. Critical for evaluating if the agent is routing tasks correctly.
instruction	string Optional. Provides instructions for the LLM model, guiding the agent's behavior. Can be static or dynamic. Dynamic instructions can contain placeholders like {variable_name} that will be resolved at runtime using the AgentEvent.state_delta field.
tools[]	Tool Optional. The list of tools available to this agent.
sub_agents[]	string Optional. The list of valid agent IDs that this agent can delegate to. This defines the directed edges in the multi-agent system graph topology.
agent_id	string Required. Unique identifier of the agent. This ID is used to refer to this agent, e.g., in AgentEvent.author, or in the sub_agents field. It must be unique within the agents map.

AgentData

Represents data specific to multi-turn agent evaluations.

Fields
agents	map<string, AgentConfig> Optional. A map containing the static configurations for each agent in the system. Key: agent_id (matches the author field in events). Value: The static configuration of the agent.
turns[]	ConversationTurn Optional. A chronological list of conversation turns. Each turn represents a logical execution cycle (e.g., User Input -> Agent Response).

AgentEvent

Represents a single event in the execution trace.

Fields
event_time	Timestamp Optional. The timestamp when the event occurred.
state_delta	Struct Optional. The change in the session state caused by this event. This is a key-value map of fields that were modified or added by the event.
active_tools[]	Tool Optional. The list of tools that were active/available to the agent at the time of this event. This overrides the AgentConfig.tools if set.
author	string Required. The ID of the agent or entity that generated this event. Use "user" to denote events generated by the end-user.
content	Content Required. The content of the event (e.g., text response, tool call, tool response).

AgentTool

A tool provides a list of actions available to the Agent during the process of executing a task.

Fields
type	string Required. The type of the tool. Supported types: code_execution filesystem google_search mcp_server url_context
name	string Optional. The name of the MCP server. Only applicable when type is mcp_server.
url	string Optional. The URL for the MCP server endpoint. Only applicable when type is mcp_server.
headers	map<string, string> Optional. The headers for the MCP server, such as for authentication. Only applicable when type is mcp_server.

AggregationOutput

The aggregation result for the entire dataset and all metrics.

Fields
dataset	EvaluationDataset The dataset used for evaluation & aggregation.
aggregation_results[]	AggregationResult One AggregationResult per metric.

AggregationResult

The aggregation result for a single metric.

Fields
aggregation_metric	AggregationMetric Aggregation metric.
Union field aggregation_result. The aggregation result. aggregation_result can be only one of the following:
pointwise_metric_result	PointwiseMetricResult Result for pointwise metric.
pairwise_metric_result	PairwiseMetricResult Result for pairwise metric.
exact_match_metric_value	ExactMatchMetricValue Results for exact match metric.
bleu_metric_value	BleuMetricValue Results for bleu metric.
rouge_metric_value	RougeMetricValue Results for rouge metric.
custom_code_execution_result	CustomCodeExecutionResult Result for code execution metric.

Annotation

Used to assign specific AnnotationSpec to a particular area of a DataItem or the whole part of the DataItem.

Fields
name	string Output only. Resource name of the Annotation.
payload_schema_uri	string Required. Google Cloud Storage URI points to a YAML file describing payload. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/, note that the chosen schema must be consistent with the parent Dataset's metadata.
payload	Value Required. The schema of the payload can be found in payload_schema.
create_time	Timestamp Output only. Timestamp when this Annotation was created.
update_time	Timestamp Output only. Timestamp when this Annotation was last updated.
etag	string Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
annotation_source	UserActionReference Output only. The source of the Annotation.
labels	map<string, string> Optional. The labels with user-defined metadata to organize your Annotations. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Annotation(System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Annotation: "aiplatform.googleapis.com/annotation_set_name": optional, name of the UI's annotation set this Annotation belongs to. If not set, the Annotation is not visible in the UI. "aiplatform.googleapis.com/payload_schema": output only, its value is the payload_schema's title.

AnnotationSpec

Identifies a concept with which DataItems may be annotated with.

Fields
name	string Output only. Resource name of the AnnotationSpec.
display_name	string Required. The user-defined name of the AnnotationSpec. The name can be up to 128 characters long and can consist of any UTF-8 characters.
create_time	Timestamp Output only. Timestamp when this AnnotationSpec was created.
update_time	Timestamp Output only. Timestamp when AnnotationSpec was last updated.
etag	string Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.

ApiAuth

The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead.

Fields
Union field auth_config. The auth config. auth_config can be only one of the following:
api_key_config	ApiKeyConfig The API secret.

ApiKeyConfig

The API secret.

Fields
api_key_secret_version	string Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}
api_key_string	string The API key string. Either this or api_key_secret_version must be set.

AppendEventRequest

Request message for SessionService.AppendEvent.

Fields
name	string Required. The resource name of the session to append event to. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}/sessions/{session}
event	SessionEvent Required. The event to append to the session.

AppendEventResponse

This type has no fields.

Response message for SessionService.AppendEvent.

Artifact

Instance of a general artifact.

Fields
name	string Output only. The resource name of the Artifact.
display_name	string User provided display name of the Artifact. May be up to 128 Unicode characters.
uri	string The uniform resource identifier of the artifact file. May be empty if there is no actual artifact file.
etag	string An eTag used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map<string, string> The labels with user-defined metadata to organize your Artifacts. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Artifact (System labels are excluded).
create_time	Timestamp Output only. Timestamp when this Artifact was created.
update_time	Timestamp Output only. Timestamp when this Artifact was last updated.
state	State The state of this Artifact. This is a property of the Artifact, and does not imply or capture any ongoing process. This property is managed by clients (such as Agent Platform Pipelines), and the system does not prescribe or check the validity of state transitions.
schema_title	string The title of the schema describing the metadata. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store.
schema_version	string The version of the schema in schema_name to use. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store.
metadata	Struct Properties of the Artifact. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB.
description	string Description of the Artifact

State

Describes the state of the Artifact.

Enums
STATE_UNSPECIFIED	Unspecified state for the Artifact.
PENDING	A state used by systems like Agent Platform Pipelines to indicate that the underlying data item represented by this Artifact is being created.
LIVE	A state indicating that the Artifact should exist, unless something external to the system deletes it.

ArtifactTypeSchema

The definition of a artifact type in MLMD.

Fields
schema_version	string The schema version of the artifact. If the value is not set, it defaults to the latest version in the system.
Union field kind. kind can be only one of the following:
schema_title	string The name of the type. The format of the title must be: <namespace>.<title>. Examples: - aiplatform.Model - acme.CustomModel When this field is set, the type must be pre-registered in the MLMD store.
schema_uri (deprecated)	string This item is deprecated! Points to a YAML file stored on Cloud Storage describing the format. Deprecated. Use [PipelineArtifactTypeSchema.schema_title][] or [PipelineArtifactTypeSchema.instance_schema][] instead.
instance_schema	string Contains a raw YAML string, describing the format of the properties of the type.

AskContextsRequest

Agentic Retrieval Ask API for RAG. Request message for VertexRagService.AskContexts.

Fields
parent	string Required. The resource name of the Location from which to retrieve RagContexts. The users must have permission to make a call in the project. Format: projects/{project}/locations/{location}.
query	RagQuery Required. Single RAG retrieve query.
tools[]	Tool Optional. The tools to use for AskContexts.

AskContextsResponse

Response message for VertexRagService.AskContexts.

Fields
response	string The Retrieval Response.
contexts	RagContexts The contexts of the query.

AssembleDataOperationMetadata

Runtime operation information for DatasetService.AssembleData.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

AssembleDataRequest

Request message for DatasetService.AssembleData. Used only for MULTIMODAL datasets.

Fields
name	string Required. The name of the Dataset resource (used only for MULTIMODAL datasets). Format: projects/{project}/locations/{location}/datasets/{dataset}
gemini_request_read_config	GeminiRequestReadConfig Optional. The read config for the dataset.

AssembleDataResponse

Response message for DatasetService.AssembleData.

Fields
bigquery_destination	string Destination BigQuery table path containing the assembled data as a single column.

AssessDataOperationMetadata

Runtime operation information for DatasetService.AssessData.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

AssessDataRequest

Request message for DatasetService.AssessData. Used only for MULTIMODAL datasets.

Fields
name	string Required. The name of the Dataset resource. Used only for MULTIMODAL datasets. Format: projects/{project}/locations/{location}/datasets/{dataset}
gemini_request_read_config	GeminiRequestReadConfig Optional. The Gemini request read config for the dataset.
Union field assessment_config. The assessment type. assessment_config can be only one of the following:
tuning_validation_assessment_config	TuningValidationAssessmentConfig Optional. Configuration for the tuning validation assessment.
tuning_resource_usage_assessment_config	TuningResourceUsageAssessmentConfig Optional. Configuration for the tuning resource usage assessment.
batch_prediction_validation_assessment_config	BatchPredictionValidationAssessmentConfig Optional. Configuration for the batch prediction validation assessment.
batch_prediction_resource_usage_assessment_config	BatchPredictionResourceUsageAssessmentConfig Optional. Configuration for the batch prediction resource usage assessment.

BatchPredictionResourceUsageAssessmentConfig

Configuration for the batch prediction resource usage assessment.

Fields
model_name	string Required. The name of the model used for batch prediction.

BatchPredictionValidationAssessmentConfig

Configuration for the batch prediction validation assessment.

Fields
model_name	string Required. The name of the model used for batch prediction.

TuningResourceUsageAssessmentConfig

Configuration for the tuning resource usage assessment.

Fields
model_name	string Required. The name of the model used for tuning.

TuningValidationAssessmentConfig

Configuration for the tuning validation assessment.

Fields
model_name	string Required. The name of the model used for tuning.
dataset_usage	DatasetUsage Required. The dataset usage (e.g. training/validation).

DatasetUsage

The dataset usage (e.g. training/validation).

Enums
DATASET_USAGE_UNSPECIFIED	Default value. Should not be used.
SFT_TRAINING	Supervised fine-tuning training dataset.
SFT_VALIDATION	Supervised fine-tuning validation dataset.

AssessDataResponse

Response message for DatasetService.AssessData.

Fields
Union field assessment_result. The assessment result. assessment_result can be only one of the following:
tuning_validation_assessment_result	TuningValidationAssessmentResult Optional. The result of the tuning validation assessment.
tuning_resource_usage_assessment_result	TuningResourceUsageAssessmentResult Optional. The result of the tuning resource usage assessment.
batch_prediction_validation_assessment_result	BatchPredictionValidationAssessmentResult Optional. The result of the batch prediction validation assessment.
batch_prediction_resource_usage_assessment_result	BatchPredictionResourceUsageAssessmentResult Optional. The result of the batch prediction resource usage assessment.

BatchPredictionResourceUsageAssessmentResult

The result of the batch prediction resource usage assessment.

Fields
token_count	int64 Number of tokens in the batch prediction dataset.
audio_token_count	int64 Number of audio tokens in the batch prediction dataset.

BatchPredictionValidationAssessmentResult

This type has no fields.

The result of the batch prediction validation assessment.

TuningResourceUsageAssessmentResult

The result of the tuning resource usage assessment.

Fields
token_count	int64 Number of tokens in the tuning dataset.
billable_character_count	int64 Number of billable tokens in the tuning dataset.

TuningValidationAssessmentResult

The result of the tuning validation assessment.

Fields
errors[]	string Optional. A list containing the first validation errors.

AssignNotebookRuntimeOperationMetadata

Metadata information for NotebookService.AssignNotebookRuntime.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.
progress_message	string A human-readable message that shows the intermediate progress details of NotebookRuntime.

AssignNotebookRuntimeRequest

Request message for NotebookService.AssignNotebookRuntime.

Fields
parent	string Required. The resource name of the Location to get the NotebookRuntime assignment. Format: projects/{project}/locations/{location}
notebook_runtime_template	string Required. The resource name of the NotebookRuntimeTemplate based on which a NotebookRuntime will be assigned (reuse or create a new one).
notebook_runtime	NotebookRuntime Required. Provide runtime specific information (e.g. runtime owner, notebook id) used for NotebookRuntime assignment.
notebook_runtime_id	string Optional. User specified ID for the notebook runtime.

AsyncQueryReasoningEngineOperationMetadata

Operation metadata message for ReasoningEngineExecutionService.AsyncQueryReasoningEngine.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

AsyncQueryReasoningEngineRequest

Request message for ReasoningEngineExecutionService.AsyncQueryReasoningEngine.

Fields
name	string Required. The name of the ReasoningEngine resource to use. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}
input_gcs_uri	string Optional. Input Cloud Storage URI for the Async query. If you are not bringing your own container (BYOC), the content of the file should be a JSON object with an input field matching the input field of QueryReasoningEngineRequest (e.g. { "input": { "user_id": "hello", "message":"$QUERY"} }). For BYOC, the content of the file depends on the the agent application.
output_gcs_uri	string Optional. Output Cloud Storage URI for the Async query. This contains the final response of the query.

AsyncQueryReasoningEngineResponse

Response message for ReasoningEngineExecutionService.AsyncQueryReasoningEngine.

Fields
output_gcs_uri	string Output Cloud Storage URI for the Async query.

AsyncRetrieveContextsOperationMetadata

Metadata for AsyncRetrieveContextsOperation.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.

AsyncRetrieveContextsRequest

Request message for VertexRagService.AsyncRetrieveContexts.

Fields
parent	string Required. The resource name of the Location from which to retrieve RagContexts. The users must have permission to make a call in the project. Format: projects/{project}/locations/{location}.
query	RagQuery Required. Single RAG retrieve query.
tools[]	Tool Optional. The tools to use for AskContexts.

AsyncRetrieveContextsResponse

Response message for VertexRagService.AsyncRetrieveContexts.

Fields
contexts	RagContexts The contexts of the query.

Attribution

Attribution that explains a particular prediction output.

Fields
baseline_output_value	double Output only. Model predicted output if the input instance is constructed from the baselines of all the features defined in ExplanationMetadata.inputs. The field name of the output is determined by the key in ExplanationMetadata.outputs. If the Model's predicted output has multiple dimensions (rank > 1), this is the value in the output located by output_index. If there are multiple baselines, their output values are averaged.
instance_output_value	double Output only. Model predicted output on the corresponding [explanation instance][ExplainRequest.instances]. The field name of the output is determined by the key in ExplanationMetadata.outputs. If the Model predicted output has multiple dimensions, this is the value in the output located by output_index.
feature_attributions	Value Output only. Attributions of each explained feature. Features are extracted from the prediction instances according to explanation metadata for inputs. The value is a struct, whose keys are the name of the feature. The values are how much the feature in the instance contributed to the predicted result. The format of the value is determined by the feature's input format: If the feature is a scalar value, the attribution value is a floating number. If the feature is an array of scalar values, the attribution value is an array. If the feature is a struct, the attribution value is a struct. The keys in the attribution value struct are the same as the keys in the feature struct. The formats of the values in the attribution struct are determined by the formats of the values in the feature struct. The ExplanationMetadata.feature_attributions_schema_uri field, pointed to by the ExplanationSpec field of the Endpoint.deployed_models object, points to the schema file that describes the features and their attribution values (if it is populated).
output_index[]	int32 Output only. The index that locates the explained prediction output. If the prediction output is a scalar value, output_index is not populated. If the prediction output has multiple dimensions, the length of the output_index list is the same as the number of dimensions of the output. The i-th element in output_index is the element index of the i-th dimension of the output vector. Indices start from 0.
output_display_name	string Output only. The display name of the output identified by output_index. For example, the predicted class name by a multi-classification Model. This field is only populated iff the Model predicts display names as a separate field along with the explained output. The predicted display name must has the same shape of the explained output, and can be located using output_index.
approximation_error	double Output only. Error of feature_attributions caused by approximation used in the explanation method. Lower value means more precise attributions. For Sampled Shapley attribution, increasing path_count might reduce the error. For Integrated Gradients attribution, increasing step_count might reduce the error. For XRAI attribution, increasing step_count might reduce the error. See this introduction for more information.
output_name	string Output only. Name of the explain output. Specified as the key in ExplanationMetadata.outputs.

AugmentPromptRequest

Request message for AugmentPrompt.

Fields
parent	string Required. The resource name of the Location from which to augment prompt. The users must have permission to make a call in the project. Format: projects/{project}/locations/{location}.
contents[]	Content Optional. Input content to augment, only text format is supported for now.
model	Model Optional. Metadata of the backend deployed model.
Union field data_source. The data source for retrieving contexts. data_source can be only one of the following:
vertex_rag_store	VertexRagStore Optional. Retrieves contexts from the Vertex RagStore.

Model

Metadata of the backend deployed model.

Fields
model	string Optional. The model that the user will send the augmented prompt for content generation.
model_version	string Optional. The model version of the backend deployed model.

AugmentPromptResponse

Response message for AugmentPrompt.

Fields
augmented_prompt[]	Content Augmented prompt, only text format is supported for now.
facts[]	Fact Retrieved facts from RAG data sources.

AuthConfig

Auth configuration to run the extension.

Fields
auth_type	AuthType Type of auth scheme.
Union field auth_config. auth_config can be only one of the following:
api_key_config	ApiKeyConfig Config for API key auth.
http_basic_auth_config	HttpBasicAuthConfig Config for HTTP Basic auth.
google_service_account_config	GoogleServiceAccountConfig Config for Google Service Account auth.
oauth_config	OauthConfig Config for user oauth.
oidc_config	OidcConfig Config for user OIDC auth.

ApiKeyConfig

Config for authentication with API key.

Fields
name	string Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.
api_key_secret	string Optional. The name of the SecretManager secret version resource storing the API key. Format: projects/{project}/secrets/{secrete}/versions/{version} If both api_key_secret and api_key_string are specified, this field takes precedence over api_key_string. If specified, the secretmanager.versions.access permission should be granted to Agent Platform Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
http_element_location	HttpElementLocation Optional. The location of the API key.

GoogleServiceAccountConfig

Config for Google Service Account Authentication.

Fields

service_account

string Optional. The service account that the extension execution service runs as. If the service account is specified, the iam.serviceAccounts.getAccessToken permission should be granted to Agent Platform Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. If not specified, the Agent Platform Extension Service Agent will be used to execute the Extension.

HttpBasicAuthConfig

Config for HTTP Basic Authentication.

Fields

credential_secret

string Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: projects/{project}/secrets/{secrete}/versions/{version} If specified, the secretmanager.versions.access permission should be granted to Agent Platform Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.

OauthConfig

Config for user oauth.

Fields
Union field oauth_config. oauth_config can be only one of the following:
access_token	string Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
service_account	string The service account used to generate access tokens for executing the Extension. If the service account is specified, the iam.serviceAccounts.getAccessToken permission should be granted to Agent Platform Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.

OidcConfig

Config for user OIDC auth.

Fields
Union field oidc_config. oidc_config can be only one of the following:
id_token	string OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
service_account	string The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). The audience for the token will be set to the URL in the server url defined in the OpenApi spec. If the service account is provided, the service account should grant iam.serviceAccounts.getOpenIdToken permission to Agent Platform Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).

AuthType

Type of Auth.

Enums
AUTH_TYPE_UNSPECIFIED
NO_AUTH	No Auth.
API_KEY_AUTH	API Key Auth.
HTTP_BASIC_AUTH	HTTP Basic Auth.
GOOGLE_SERVICE_ACCOUNT_AUTH	Google Service Account Auth.
OAUTH	OAuth auth.
OIDC_AUTH	OpenID Connect (OIDC) Auth.

AutomaticResources

A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration. Each Model supporting these resources documents its specific guidelines.

Fields
min_replica_count	int32 Immutable. The minimum number of replicas that will be always deployed on. If traffic against it increases, it may dynamically be deployed onto more replicas up to max_replica_count, and as traffic decreases, some of these extra replicas may be freed. If the requested value is too large, the deployment will error.
max_replica_count	int32 Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, a no upper bound for scaling under heavy traffic will be assume, though Agent Platform may be unable to scale beyond certain replica number.

AutoraterConfig

The configs for autorater. This is applicable to both EvaluateInstances and EvaluateDataset.

Fields
autorater_model	string Optional. The fully qualified name of the publisher model or tuned autorater endpoint to use. Publisher model format: projects/{project}/locations/{location}/publishers/*/models/* Tuned model endpoint format: projects/{project}/locations/{location}/endpoints/{endpoint}
generation_config	GenerationConfig Optional. Configuration options for model generation and outputs.
sampling_count	int32 Optional. Number of samples for each instance in the dataset. If not specified, the default is 4. Minimum value is 1, maximum value is 32.
flip_enabled	bool Optional. Default is true. Whether to flip the candidate and baseline responses. This is only applicable to the pairwise metric. If enabled, also provide PairwiseMetricSpec.candidate_response_field_name and PairwiseMetricSpec.baseline_response_field_name. When rendering PairwiseMetricSpec.metric_prompt_template, the candidate and baseline fields will be flipped for half of the samples to reduce bias.

AutoscalingMetricSpec

The metric specification that defines the target resource utilization (CPU utilization, accelerator's duty cycle, and so on) for calculating the desired replica count.

Fields
metric_name	string Required. The resource metric name. Supported metrics: For Online Prediction: aiplatform.googleapis.com/prediction/online/accelerator/duty_cycle aiplatform.googleapis.com/prediction/online/cpu/utilization aiplatform.googleapis.com/prediction/online/request_count pubsub.googleapis.com/subscription/num_undelivered_messages prometheus.googleapis.com/vertex_dcgm_fi_dev_gpu_util prometheus.googleapis.com/vertex_vllm_gpu_cache_usage_perc prometheus.googleapis.com/vertex_vllm_num_requests_waiting
target	int32 The target resource utilization in percentage (1% - 100%) for the given metric; once the real usage deviates from the target by a certain percentage, the machine replicas change. The default value is 60 (representing 60%) if not provided.
monitored_resource_labels	map<string, string> Optional. The Cloud Monitoring monitored resource labels as key value pairs used for metrics filtering. See Cloud Monitoring Labels https://cloud.google.com/monitoring/api/v3/metric-model#generic-label-info

AvroSource

The storage details for Avro input content.

Fields
gcs_source	GcsSource Required. Google Cloud Storage location.

BatchCancelPipelineJobsOperationMetadata

Runtime operation information for PipelineService.BatchCancelPipelineJobs.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

BatchCancelPipelineJobsRequest

Request message for PipelineService.BatchCancelPipelineJobs.

Fields
parent	string Required. The name of the PipelineJobs' parent resource. Format: projects/{project}/locations/{location}
names[]	string Required. The names of the PipelineJobs to cancel. A maximum of 32 PipelineJobs can be cancelled in a batch. Format: projects/{project}/locations/{location}/pipelineJobs/{pipelineJob}

BatchCancelPipelineJobsResponse

Response message for PipelineService.BatchCancelPipelineJobs.

Fields
pipeline_jobs[]	PipelineJob PipelineJobs cancelled.

BatchCreateFeaturesOperationMetadata

Details of operations that perform batch create Features.

Fields
generic_metadata	GenericOperationMetadata Operation metadata for Feature.

BatchCreateFeaturesRequest

Request message for FeaturestoreService.BatchCreateFeatures. Request message for FeatureRegistryService.BatchCreateFeatures.

Fields
parent	string Required. The resource name of the EntityType/FeatureGroup to create the batch of Features under. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} projects/{project}/locations/{location}/featureGroups/{feature_group}
requests[]	CreateFeatureRequest Required. The request message specifying the Features to create. All Features must be created under the same parent EntityType / FeatureGroup. The parent field in each child request message can be omitted. If parent is set in a child request, then the value must match the parent value in this request message.

BatchCreateFeaturesResponse

Response message for FeaturestoreService.BatchCreateFeatures.

Fields
features[]	Feature The Features created.

BatchCreateRagDataSchemasOperationMetadata

Runtime operation information for VertexRagDataService.BatchCreateRagDataSchemas.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.

BatchCreateRagDataSchemasRequest

Request message for VertexRagDataService.BatchCreateRagDataSchemas.

Fields
parent	string Required. The resource name of the RagCorpus to create the RagDataSchemas in. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}
requests[]	CreateRagDataSchemaRequest Required. The request messages for VertexRagDataService.CreateRagDataSchema. A maximum of 500 schemas can be created in a batch.

BatchCreateRagDataSchemasResponse

Response message for VertexRagDataService.BatchCreateRagDataSchemas.

Fields
rag_data_schemas[]	RagDataSchema RagDataSchemas created.

BatchCreateRagMetadataOperationMetadata

Runtime operation information for VertexRagDataService.BatchCreateRagMetadata.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.

BatchCreateRagMetadataRequest

Request message for VertexRagDataService.BatchCreateRagMetadata.

Fields
parent	string Required. The parent resource where the RagMetadata will be created. Format: projects/{project_number}/locations/{location_id}/ragCorpora/{rag_corpus}/ragFiles/{rag_file}
requests[]	CreateRagMetadataRequest Required. The request messages for VertexRagDataService.CreateRagMetadata. A maximum of 500 rag file metadata can be created in a batch.

BatchCreateRagMetadataResponse

Response message for VertexRagDataService.BatchCreateRagMetadata.

Fields
rag_metadata[]	RagMetadata RagMetadata created.

BatchCreateTensorboardRunsRequest

Request message for TensorboardService.BatchCreateTensorboardRuns.

Fields
parent	string Required. The resource name of the TensorboardExperiment to create the TensorboardRuns in. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment} The parent field in the CreateTensorboardRunRequest messages must match this field.
requests[]	CreateTensorboardRunRequest Required. The request message specifying the TensorboardRuns to create. A maximum of 1000 TensorboardRuns can be created in a batch.

BatchCreateTensorboardRunsResponse

Response message for TensorboardService.BatchCreateTensorboardRuns.

Fields
tensorboard_runs[]	TensorboardRun The created TensorboardRuns.

BatchCreateTensorboardTimeSeriesRequest

Request message for TensorboardService.BatchCreateTensorboardTimeSeries.

Fields
parent	string Required. The resource name of the TensorboardExperiment to create the TensorboardTimeSeries in. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment} The TensorboardRuns referenced by the parent fields in the CreateTensorboardTimeSeriesRequest messages must be sub resources of this TensorboardExperiment.
requests[]	CreateTensorboardTimeSeriesRequest Required. The request message specifying the TensorboardTimeSeries to create. A maximum of 1000 TensorboardTimeSeries can be created in a batch.

BatchCreateTensorboardTimeSeriesResponse

Response message for TensorboardService.BatchCreateTensorboardTimeSeries.

Fields
tensorboard_time_series[]	TensorboardTimeSeries The created TensorboardTimeSeries.

BatchDedicatedResources

A description of resources that are used for performing batch operations, are dedicated to a Model, and need manual configuration.

Fields
machine_spec	MachineSpec Required. Immutable. The specification of a single machine.
starting_replica_count	int32 Immutable. The number of machine replicas used at the start of the batch operation. If not set, Agent Platform decides starting number, not greater than max_replica_count
max_replica_count	int32 Immutable. The maximum number of machine replicas the batch operation may be scaled to. The default value is 10.
flex_start	FlexStart Optional. Immutable. If set, use DWS resource to schedule the deployment workload. reference: (https://cloud.google.com/blog/products/compute/introducing-dynamic-workload-scheduler)
spot	bool Optional. If true, schedule the deployment workload on spot VMs.

BatchDeletePipelineJobsRequest

Request message for PipelineService.BatchDeletePipelineJobs.

Fields
parent	string Required. The name of the PipelineJobs' parent resource. Format: projects/{project}/locations/{location}
names[]	string Required. The names of the PipelineJobs to delete. A maximum of 32 PipelineJobs can be deleted in a batch. Format: projects/{project}/locations/{location}/pipelineJobs/{pipelineJob}

BatchDeletePipelineJobsResponse

Response message for PipelineService.BatchDeletePipelineJobs.

Fields
pipeline_jobs[]	PipelineJob PipelineJobs deleted.

BatchDeleteRagDataSchemasRequest

Request message for VertexRagDataService.BatchDeleteRagDataSchemas.

Fields
parent	string Required. The resource name of the RagCorpus from which to delete the RagDataSchemas. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}
names[]	string Required. The RagDataSchemas to delete. A maximum of 500 schemas can be deleted in a batch. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}/ragDataSchemas/{rag_data_schema}

BatchDeleteRagMetadataRequest

Request message for VertexRagDataService.BatchDeleteRagMetadata.

Fields
parent	string Required. The resource name of the RagFile from which to delete the RagMetadata. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}/ragFiles/{rag_file}
names[]	string Required. The RagMetadata to delete. A maximum of 500 rag metadata can be deleted in a batch.

BatchImportEvaluatedAnnotationsRequest

Request message for ModelService.BatchImportEvaluatedAnnotations

Fields
parent	string Required. The name of the parent ModelEvaluationSlice resource. Format: projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}/slices/{slice}
evaluated_annotations[]	EvaluatedAnnotation Required. Evaluated annotations resource to be imported.

BatchImportEvaluatedAnnotationsResponse

Response message for ModelService.BatchImportEvaluatedAnnotations

Fields
imported_evaluated_annotations_count	int32 Output only. Number of EvaluatedAnnotations imported.

BatchImportModelEvaluationSlicesRequest

Request message for ModelService.BatchImportModelEvaluationSlices

Fields
parent	string Required. The name of the parent ModelEvaluation resource. Format: projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}
model_evaluation_slices[]	ModelEvaluationSlice Required. Model evaluation slice resource to be imported.

BatchImportModelEvaluationSlicesResponse

Response message for ModelService.BatchImportModelEvaluationSlices

Fields
imported_model_evaluation_slices[]	string Output only. List of imported ModelEvaluationSlice.name.

BatchMigrateResourcesOperationMetadata

Runtime operation information for MigrationService.BatchMigrateResources.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.
partial_results[]	PartialResult Partial results that reflect the latest migration operation progress.

PartialResult

Represents a partial result in batch migration operation for one MigrateResourceRequest.

Fields
request	MigrateResourceRequest It's the same as the value in BatchMigrateResourcesRequest.migrate_resource_requests.
Union field result. If the resource's migration is ongoing, none of the result will be set. If the resource's migration is finished, either error or one of the migrated resource name will be filled. result can be only one of the following:
error	Status The error result of the migration request in case of failure.
model	string Migrated model resource name.
dataset	string Migrated dataset resource name.

BatchMigrateResourcesRequest

Request message for MigrationService.BatchMigrateResources.

Fields
parent	string Required. The location of the migrated resource will live in. Format: projects/{project}/locations/{location}
migrate_resource_requests[]	MigrateResourceRequest Required. The request messages specifying the resources to migrate. They must be in the same location as the destination. Up to 50 resources can be migrated in one batch.

BatchMigrateResourcesResponse

Response message for MigrationService.BatchMigrateResources.

Fields
migrate_resource_responses[]	MigrateResourceResponse Successfully migrated resources.

BatchPredictionJob

A job that uses a Model to produce predictions on multiple input instances. If predictions for significant portion of the instances fail, the job may finish without attempting predictions for all remaining instances.

Fields
name	string Output only. Resource name of the BatchPredictionJob.
display_name	string Required. The user-defined name of this BatchPredictionJob.
model	string The name of the Model resource that produces the predictions via this job, must share the same ancestor Location. Starting this job has no impact on any existing deployments of the Model and their resources. Exactly one of model, unmanaged_container_model, or endpoint must be set. The model resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed. The model resource could also be a publisher model. Example: publishers/{publisher}/models/{model} or projects/{project}/locations/{location}/publishers/{publisher}/models/{model}
model_version_id	string Output only. The version ID of the Model that produces the predictions via this job.
unmanaged_container_model	UnmanagedContainerModel Contains model information necessary to perform batch prediction without requiring uploading to model registry. Exactly one of model, unmanaged_container_model, or endpoint must be set.
input_config	InputConfig Required. Input configuration of the instances on which predictions are performed. The schema of any single instance may be specified via the Model's PredictSchemata's instance_schema_uri.
instance_config	InstanceConfig Configuration for how to convert batch prediction input instances to the prediction instances that are sent to the Model.
model_parameters	Value The parameters that govern the predictions. The schema of the parameters may be specified via the Model's PredictSchemata's parameters_schema_uri.
output_config	OutputConfig Required. The Configuration specifying where output predictions should be written. The schema of any single prediction may be specified as a concatenation of Model's PredictSchemata's instance_schema_uri and prediction_schema_uri.
dedicated_resources	BatchDedicatedResources The config of resources used by the Model during the batch prediction. If the Model supports DEDICATED_RESOURCES this config may be provided (and the job will use these resources), if the Model doesn't support AUTOMATIC_RESOURCES, this config must be provided.
service_account	string The service account that the DeployedModel's container runs as. If not specified, a system generated one will be used, which has minimal permissions and the custom container, if used, may not have enough permission to access other Google Cloud resources. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
manual_batch_tuning_parameters	ManualBatchTuningParameters Immutable. Parameters configuring the batch behavior. Currently only applicable when dedicated_resources are used (in other cases Agent Platform does the tuning itself).
generate_explanation	bool Generate explanation with the batch prediction results. When set to true, the batch prediction output changes based on the predictions_format field of the BatchPredictionJob.output_config object: bigquery: output includes a column named explanation. The value is a struct that conforms to the Explanation object. jsonl: The JSON objects on each line include an additional entry keyed explanation. The value of the entry is a JSON object that conforms to the Explanation object. csv: Generating explanations for CSV format is not supported. If this field is set to true, either the Model.explanation_spec or explanation_spec must be populated.
explanation_spec	ExplanationSpec Explanation configuration for this BatchPredictionJob. Can be specified only if generate_explanation is set to true. This value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of the explanation_spec object is not populated, the corresponding field of the Model.explanation_spec object is inherited.
output_info	OutputInfo Output only. Information further describing the output of this job.
state	JobState Output only. The detailed state of the job.
error	Status Output only. Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
partial_failures[]	Status Output only. Partial failures encountered. For example, single files that can't be read. This field never exceeds 20 entries. Status details fields contain standard Google Cloud error details.
resources_consumed	ResourcesConsumed Output only. Information about resources that had been consumed by this job. Provided in real time at best effort basis, as well as a final value once the job completes. Note: This field currently may be not populated for batch predictions that use AutoML Models.
completion_stats	CompletionStats Output only. Statistics on completed and failed prediction instances.
create_time	Timestamp Output only. Time when the BatchPredictionJob was created.
start_time	Timestamp Output only. Time when the BatchPredictionJob for the first time entered the JOB_STATE_RUNNING state.
end_time	Timestamp Output only. Time when the BatchPredictionJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
update_time	Timestamp Output only. Time when the BatchPredictionJob was most recently updated.
labels	map<string, string> The labels with user-defined metadata to organize BatchPredictionJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
encryption_spec	EncryptionSpec Customer-managed encryption key options for a BatchPredictionJob. If this is set, then all resources created by the BatchPredictionJob will be encrypted with the provided encryption key.
model_monitoring_config	ModelMonitoringConfig Model monitoring config will be used for analysis model behaviors, based on the input and output to the batch prediction job, as well as the provided training dataset.
model_monitoring_stats_anomalies[]	ModelMonitoringStatsAnomalies Get batch prediction job monitoring statistics.
model_monitoring_status	Status Output only. The running status of the model monitoring pipeline.
disable_container_logging	bool For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
satisfies_pzs	bool Output only. Reserved for future use.
satisfies_pzi	bool Output only. Reserved for future use.

InputConfig

Configures the input to BatchPredictionJob. See Model.supported_input_storage_formats for Model's supported input formats, and how instances should be expressed via any of them.

Fields
instances_format	string Required. The format in which instances are given, must be one of the Model's supported_input_storage_formats.
Union field source. Required. The source of the input. source can be only one of the following:
gcs_source	GcsSource The Cloud Storage location for the input instances.
bigquery_source	BigQuerySource The BigQuery location of the input table. The schema of the table should be in the format described by the given context OpenAPI Schema, if one is provided. The table may contain additional columns that are not described by the schema, and they will be ignored.
vertex_multimodal_dataset_source	VertexMultimodalDatasetSource A Vertex Managed Dataset. Currently, only datasets of type Multimodal are supported.

InstanceConfig

Configuration defining how to transform batch prediction input instances to the instances that the Model accepts.

Fields
instance_type	string The format of the instance that the Model accepts. Agent Platform will convert compatible batch prediction input instance formats to the specified format. Supported values are: object: Each input is converted to JSON object format. For bigquery, each row is converted to an object. For jsonl, each line of the JSONL input must be an object. Does not apply to csv, file-list, tf-record, or tf-record-gzip. array: Each input is converted to JSON array format. For bigquery, each row is converted to an array. The order of columns is determined by the BigQuery column order, unless included_fields is populated. included_fields must be populated for specifying field orders. For jsonl, if each line of the JSONL input is an object, included_fields must be populated for specifying field orders. Does not apply to csv, file-list, tf-record, or tf-record-gzip. If not specified, Agent Platform converts the batch prediction input as follows: For bigquery and csv, the behavior is the same as array. The order of columns is the same as defined in the file or table, unless included_fields is populated. For jsonl, the prediction instance format is determined by each line of the input. For tf-record/tf-record-gzip, each record will be converted to an object in the format of {"b64": <value>}, where <value> is the Base64-encoded string of the content of the record. For file-list, each file in the list will be converted to an object in the format of {"b64": <value>}, where <value> is the Base64-encoded string of the content of the file.
key_field	string The name of the field that is considered as a key. The values identified by the key field is not included in the transformed instances that is sent to the Model. This is similar to specifying this name of the field in excluded_fields. In addition, the batch prediction output will not include the instances. Instead the output will only include the value of the key field, in a field named key in the output: For jsonl output format, the output will have a key field instead of the instance field. For csv/bigquery output format, the output will have have a key column instead of the instance feature columns. The input must be JSONL with objects at each line, CSV, BigQuery or TfRecord.
included_fields[]	string Fields that will be included in the prediction instance that is sent to the Model. If instance_type is array, the order of field names in included_fields also determines the order of the values in the array. When included_fields is populated, excluded_fields must be empty. The input must be JSONL with objects at each line, BigQuery or TfRecord.
excluded_fields[]	string Fields that will be excluded in the prediction instance that is sent to the Model. Excluded will be attached to the batch prediction output if key_field is not specified. When excluded_fields is populated, included_fields must be empty. The input must be JSONL with objects at each line, BigQuery or TfRecord.

OutputConfig

Configures the output of BatchPredictionJob. See Model.supported_output_storage_formats for supported output formats, and how predictions are expressed via any of them.

Fields
predictions_format	string Required. The format in which Agent Platform gives the predictions, must be one of the Model's supported_output_storage_formats.
Union field destination. Required. The destination of the output. destination can be only one of the following:
gcs_destination	GcsDestination The Cloud Storage location of the directory where the output is to be written to. In the given directory a new directory is created. Its name is prediction-<model-display-name>-<job-create-time>, where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. Inside of it files predictions_0001.<extension>, predictions_0002.<extension>, ..., predictions_N.<extension> are created where <extension> depends on chosen predictions_format, and N may equal 0001 and depends on the total number of successfully predicted instances. If the Model has both instance and prediction schemata defined then each such file contains predictions as per the predictions_format. If prediction for any instance failed (partially or completely), then an additional errors_0001.<extension>, errors_0002.<extension>,..., errors_N.<extension> files are created (N depends on total number of failed predictions). These files contain the failed instances, as per their schema, followed by an additional error field which as value has google.rpc.Status containing only code and message fields.
bigquery_destination	BigQueryDestination The BigQuery project or dataset location where the output is to be written to. If project is provided, a new dataset is created with name prediction_<model-display-name>_<job-create-time> where is made BigQuery-dataset-name compatible (for example, most special characters become underscores), and timestamp is in YYYY_MM_DDThh_mm_ss_sssZ "based on ISO-8601" format. In the dataset two tables will be created, predictions, and errors. If the Model has both instance and prediction schemata defined then the tables have columns as follows: The predictions table contains instances for which the prediction succeeded, it has columns as per a concatenation of the Model's instance and prediction schemata. The errors table contains rows for which the prediction has failed, it has instance columns, as per the instance schema, followed by a single "errors" column, which as values has google.rpc.Status represented as a STRUCT, and containing only code and message.
vertex_multimodal_dataset_destination	VertexMultimodalDatasetDestination The details for a Vertex Multimodal Dataset that will be created for the output.

OutputInfo

Further describes this job's output. Supplements output_config.

Fields
bigquery_output_table	string Output only. The name of the BigQuery table created, in predictions_<timestamp> format, into which the prediction output is written. Can be used by UI to generate the BigQuery output path, for example.
Union field output_location. The output location into which prediction output is written. output_location can be only one of the following:
gcs_output_directory	string Output only. The full path of the Cloud Storage directory created, into which the prediction output is written.
bigquery_output_dataset	string Output only. The path of the BigQuery dataset created, in bq://projectId.bqDatasetId format, into which the prediction output is written.
vertex_multimodal_dataset_name	string Output only. The resource name of the Vertex Managed Dataset created, into which the prediction output is written. Format: projects/{project}/locations/{location}/datasets/{dataset}

BatchReadFeatureValuesOperationMetadata

Details of operations that batch reads Feature values.

Fields
generic_metadata	GenericOperationMetadata Operation metadata for Featurestore batch read Features values.

BatchReadFeatureValuesRequest

Request message for FeaturestoreService.BatchReadFeatureValues.

Fields
featurestore	string Required. The resource name of the Featurestore from which to query Feature values. Format: projects/{project}/locations/{location}/featurestores/{featurestore}
destination	FeatureValueDestination Required. Specifies output location and format.
pass_through_fields[]	PassThroughField When not empty, the specified fields in the *_read_instances source will be joined as-is in the output, in addition to those fields from the Featurestore Entity. For BigQuery source, the type of the pass-through values will be automatically inferred. For CSV source, the pass-through values will be passed as opaque bytes.
entity_type_specs[]	EntityTypeSpec Required. Specifies EntityType grouping Features to read values of and settings.
start_time	Timestamp Optional. Excludes Feature values with feature generation timestamp before this timestamp. If not set, retrieve oldest values kept in Feature Store. Timestamp, if present, must not have higher than millisecond precision.
Union field read_option. read_option can be only one of the following:
csv_read_instances	CsvSource Each read instance consists of exactly one read timestamp and one or more entity IDs identifying entities of the corresponding EntityTypes whose Features are requested. Each output instance contains Feature values of requested entities concatenated together as of the read time. An example read instance may be foo_entity_id, bar_entity_id, 2020-01-01T10:00:00.123Z. An example output instance may be foo_entity_id, bar_entity_id, 2020-01-01T10:00:00.123Z, foo_entity_feature1_value, bar_entity_feature2_value. Timestamp in each read instance must be millisecond-aligned. csv_read_instances are read instances stored in a plain-text CSV file. The header should be: [ENTITY_TYPE_ID1], [ENTITY_TYPE_ID2], ..., timestamp The columns can be in any order. Values in the timestamp column must use the RFC 3339 format, e.g. 2012-07-30T10:43:17.123Z.
bigquery_read_instances	BigQuerySource Similar to csv_read_instances, but from BigQuery source.

EntityTypeSpec

Selects Features of an EntityType to read values of and specifies read settings.

Fields
entity_type_id	string Required. ID of the EntityType to select Features. The EntityType id is the entity_type_id specified during EntityType creation.
feature_selector	FeatureSelector Required. Selectors choosing which Feature values to read from the EntityType.
settings[]	DestinationFeatureSetting Per-Feature settings for the batch read.

PassThroughField

Describe pass-through fields in read_instance source.

Fields
field_name	string Required. The name of the field in the CSV header or the name of the column in BigQuery table. The naming restriction is the same as Feature.name.

BatchReadFeatureValuesResponse

This type has no fields.

Response message for FeaturestoreService.BatchReadFeatureValues.

BatchReadTensorboardTimeSeriesDataRequest

Request message for TensorboardService.BatchReadTensorboardTimeSeriesData.

Fields
tensorboard	string Required. The resource name of the Tensorboard containing TensorboardTimeSeries to read data from. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}. The TensorboardTimeSeries referenced by time_series must be sub resources of this Tensorboard.
time_series[]	string Required. The resource names of the TensorboardTimeSeries to read data from. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}

BatchReadTensorboardTimeSeriesDataResponse

Response message for TensorboardService.BatchReadTensorboardTimeSeriesData.

Fields
time_series_data[]	TimeSeriesData The returned time series data.

BidiInvokeReasoningEngineRequest

Request message for ReasoningEngineExecutionService.BidiInvokeReasoningEngine.

Fields
name	string Optional. The name of the ReasoningEngine. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine} or projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}/runtimeRevisions/{runtime_revision}
http_body	HttpBody Optional. The invoke method input. Supports arbitrary data payload.

BigQueryDestination

The BigQuery location for the output content.

Fields
output_uri	string Required. BigQuery URI to a project or table, up to 2000 characters long. When only the project is specified, the Dataset and Table is created. When the full table reference is specified, the Dataset must exist and table must not exist. Accepted forms: BigQuery path. For example: bq://projectId or bq://projectId.bqDatasetId or bq://projectId.bqDatasetId.bqTableId.

BigQuerySource

The BigQuery location for the input content.

Fields
input_uri	string Required. BigQuery URI to a table, up to 2000 characters long. Accepted forms: BigQuery path. For example: bq://projectId.bqDatasetId.bqTableId.

BleuInput

Input for bleu metric.

Fields
metric_spec	BleuSpec Required. Spec for bleu score metric.
instances[]	BleuInstance Required. Repeated bleu instances.

BleuInstance

Spec for bleu instance.

Fields
prediction	string Required. Output of the evaluated model.
reference	string Required. Ground truth used to compare against the prediction.

BleuMetricValue

Bleu metric value for an instance.

Fields
score	float Output only. Bleu score.

BleuResults

Results for bleu metric.

Fields
bleu_metric_values[]	BleuMetricValue Output only. Bleu metric values.

BleuSpec

Spec for bleu score metric - calculates the precision of n-grams in the prediction as compared to reference - returns a score ranging between 0 to 1.

Fields
use_effective_order	bool Optional. Whether to use_effective_order to compute bleu score.

Blob

A content blob.

A Blob contains data of a specific media type. It is used to represent images, audio, and video.

Fields
mime_type	string Required. The IANA standard MIME type of the source data.
data	bytes Required. The raw bytes of the data.
display_name	string Optional. The display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is used in the Gemini calls only when server-side tools (code_execution, google_search, and url_context) are enabled.

BlurBaselineConfig

Config for blur baseline.

When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383

Fields
max_blur_sigma	float The standard deviation of the blur kernel for the blurred baseline. The same blurring parameter is used for both the height and the width dimension. If not set, the method defaults to the zero (i.e. black for images) baseline.

BoolArray

A list of boolean values.

Fields
values[]	bool A list of bool values.

CachedContent

A resource used in LLM queries for users to explicitly specify what to cache and how to cache.

Fields
name	string Immutable. Identifier. The server-generated resource name of the cached content Format: projects/{project}/locations/{location}/cachedContents/{cached_content}
display_name	string Optional. Immutable. The user-generated meaningful display name of the cached content.
model	string Immutable. The name of the Model to use for cached content. Currently, only the published Gemini base models are supported, in form of projects/{PROJECT}/locations/{LOCATION}/publishers/google/models/{MODEL}
system_instruction	Content Optional. Input only. Immutable. Developer set system instruction. Currently, text only
contents[]	Content Optional. Input only. Immutable. The content to cache
tools[]	Tool Optional. Input only. Immutable. A list of Tools the model may use to generate the next response
tool_config	ToolConfig Optional. Input only. Immutable. Tool config. This config is shared for all tools
create_time	Timestamp Output only. Creation time of the cache entry.
update_time	Timestamp Output only. When the cache entry was last updated in UTC time.
usage_metadata	UsageMetadata Output only. Metadata on the usage of the cached content.
encryption_spec	EncryptionSpec Input only. Immutable. Customer-managed encryption key spec for a CachedContent. If set, this CachedContent and all its sub-resources will be secured by this key.
Union field expiration. Expiration time of the cached content. expiration can be only one of the following:
expire_time	Timestamp Timestamp of when this resource is considered expired. This is always provided on output, regardless of what was sent on input.
ttl	Duration Input only. The TTL for this resource. The expiration time is computed: now + TTL.

UsageMetadata

Metadata on the usage of the cached content.

Fields
total_token_count	int32 Total number of tokens that the cached content consumes.
text_count	int32 Number of text characters.
image_count	int32 Number of images.
video_duration_seconds	int32 Duration of video in seconds.
audio_duration_seconds	int32 Duration of audio in seconds.

CancelAsyncQueryReasoningEngineRequest

Request message for ReasoningEngineExecutionService.CancelAsyncQueryReasoningEngine.

Fields
name	string Required. The name of the ReasoningEngine resource to use. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}
operation_name	string Required. The name of the longrunning operation returned from AsyncQueryReasoningEngine. Format: projects/{project}/locations/{location}/operations/{operation}

CancelAsyncQueryReasoningEngineResponse

This type has no fields.

Response message for ReasoningEngineExecutionService.CancelAsyncQueryReasoningEngine.

CancelBatchPredictionJobRequest

Request message for JobService.CancelBatchPredictionJob.

Fields
name	string Required. The name of the BatchPredictionJob to cancel. Format: projects/{project}/locations/{location}/batchPredictionJobs/{batch_prediction_job}

CancelCustomJobRequest

Request message for JobService.CancelCustomJob.

Fields
name	string Required. The name of the CustomJob to cancel. Format: projects/{project}/locations/{location}/customJobs/{custom_job}

CancelHyperparameterTuningJobRequest

Request message for JobService.CancelHyperparameterTuningJob.

Fields
name	string Required. The name of the HyperparameterTuningJob to cancel. Format: projects/{project}/locations/{location}/hyperparameterTuningJobs/{hyperparameter_tuning_job}

CancelPipelineJobRequest

Request message for PipelineService.CancelPipelineJob.

Fields
name	string Required. The name of the PipelineJob to cancel. Format: projects/{project}/locations/{location}/pipelineJobs/{pipeline_job}

CancelTrainingPipelineRequest

Request message for PipelineService.CancelTrainingPipeline.

Fields
name	string Required. The name of the TrainingPipeline to cancel. Format: projects/{project}/locations/{location}/trainingPipelines/{training_pipeline}

CancelTuningJobRequest

Request message for GenAiTuningService.CancelTuningJob.

Fields
name	string Required. The name of the tuning job to cancel. Format: projects/{project}/locations/{location}/tuningJobs/{tuning_job}

Candidate

A response candidate generated from the model.

Fields
index	int32 Output only. The 0-based index of this candidate in the list of generated responses. This is useful for distinguishing between multiple candidates when candidate_count > 1.
content	Content Output only. The content of the candidate.
avg_logprobs	double Output only. The average log probability of the tokens in this candidate. This is a length-normalized score that can be used to compare the quality of candidates of different lengths. A higher average log probability suggests a more confident and coherent response.
logprobs_result	LogprobsResult Output only. The detailed log probability information for the tokens in this candidate. This is useful for debugging, understanding model uncertainty, and identifying potential "hallucinations".
finish_reason	FinishReason Output only. The reason why the model stopped generating tokens. If empty, the model has not stopped generating.
safety_ratings[]	SafetyRating Output only. A list of ratings for the safety of a response candidate. There is at most one rating per category.
citation_metadata	CitationMetadata Output only. A collection of citations that apply to the generated content.
grounding_metadata	GroundingMetadata Output only. Metadata returned when grounding is enabled. It contains the sources used to ground the generated content.
url_context_metadata	UrlContextMetadata Output only. Metadata returned when the model uses the url_context tool to get information from a user-provided URL.
finish_message	string Output only. Describes the reason the model stopped generating tokens in more detail. This field is returned only when finish_reason is set.

FinishReason

The reason why the model stopped generating tokens. If this field is empty, the model has not stopped generating.

Enums
FINISH_REASON_UNSPECIFIED	The finish reason is unspecified.
STOP	The model reached a natural stopping point or a configured stop sequence.
MAX_TOKENS	The model generated the maximum number of tokens allowed by the max_output_tokens parameter.
SAFETY	The model stopped generating because the content potentially violates safety policies. NOTE: When streaming, the content field is empty if content filters block the output.
RECITATION	The model stopped generating because the content may be a recitation from a source.
OTHER	The model stopped generating for a reason not otherwise specified.
BLOCKLIST	The model stopped generating because the content contains a term from a configured blocklist.
PROHIBITED_CONTENT	The model stopped generating because the content may be prohibited.
SPII	The model stopped generating because the content may contain sensitive personally identifiable information (SPII).
MALFORMED_FUNCTION_CALL	The model generated a function call that is syntactically invalid and can't be parsed.
MODEL_ARMOR	The model response was blocked by Model Armor.
IMAGE_SAFETY	The generated image potentially violates safety policies.
IMAGE_PROHIBITED_CONTENT	The generated image may contain prohibited content.
IMAGE_RECITATION	The generated image may be a recitation from a source.
IMAGE_OTHER	The image generation stopped for a reason not otherwise specified.
UNEXPECTED_TOOL_CALL	The model generated a function call that is semantically invalid. This can happen, for example, if function calling is not enabled or the generated function is not in the function declaration.
NO_IMAGE	The model was expected to generate an image, but didn't.

ChatCompletionsRequest

Request message for [PredictionService.ChatCompletions]

Fields
endpoint	string Required. The name of the endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
http_body	HttpBody Optional. The prediction input. Supports HTTP headers and a JSON body in the OpenAI-compatible chat completions format.

CheckPublisherModelEulaAcceptanceRequest

Request message for [ModelGardenService.CheckPublisherModelEula][].

Fields
parent	string Required. The project requesting access for named model. The format is projects/{project}.
publisher_model	string Required. The name of the PublisherModel resource. Format: publishers/{publisher}/models/{publisher_model}, or publishers/hf-{hugging-face-author}/models/{hugging-face-model-name}

CheckTrialEarlyStoppingStateMetatdata

This message will be placed in the metadata field of a google.longrunning.Operation associated with a CheckTrialEarlyStoppingState request.

Fields
generic_metadata	GenericOperationMetadata Operation metadata for suggesting Trials.
study	string The name of the Study that the Trial belongs to.
trial	string The Trial name.

CheckTrialEarlyStoppingStateRequest

Request message for VizierService.CheckTrialEarlyStoppingState.

Fields
trial_name	string Required. The Trial's name. Format: projects/{project}/locations/{location}/studies/{study}/trials/{trial}

CheckTrialEarlyStoppingStateResponse

Response message for VizierService.CheckTrialEarlyStoppingState.

Fields
should_stop	bool True if the Trial should stop.

Checkpoint

Describes the machine learning model version checkpoint.

Fields
checkpoint_id	string The ID of the checkpoint.
epoch	int64 The epoch of the checkpoint.
step	int64 The step of the checkpoint.

Citation

A citation for a piece of generatedcontent.

Fields
start_index	int32 Output only. The start index of the citation in the content.
end_index	int32 Output only. The end index of the citation in the content.
uri	string Output only. The URI of the source of the citation.
title	string Output only. The title of the source of the citation.
license	string Output only. The license of the source of the citation.
publication_date	Date Output only. The publication date of the source of the citation.

CitationMetadata

A collection of citations that apply to a piece of generated content.

Fields
citations[]	Citation Output only. A list of citations for the content.

Claim

Claim that is extracted from the input text and facts that support it.

Fields
fact_indexes[]	int32 Indexes of the facts supporting this claim.
start_index	int32 Index in the input text where the claim starts (inclusive).
end_index	int32 Index in the input text where the claim ends (exclusive).
score	float Confidence score of this corroboration.

ClientConnectionConfig

Configurations (e.g. inference timeout) that are applied on your endpoints.

Fields
inference_timeout	Duration Customizable online prediction request timeout.

CodeExecutionResult

Result of executing the ExecutableCode.

Generated only when the CodeExecution tool is used.

Fields
outcome	Outcome Required. Outcome of the code execution.
output	string Optional. Contains stdout when code execution is successful, stderr or other description otherwise.

Outcome

Enumeration of possible outcomes of the code execution.

Enums
OUTCOME_UNSPECIFIED	Unspecified status. This value should not be used.
OUTCOME_OK	Code execution completed successfully. output contains the stdout, if any.
OUTCOME_FAILED	Code execution failed. output contains the stderr and stdout, if any.
OUTCOME_DEADLINE_EXCEEDED	Code execution ran for too long, and was cancelled. There may or may not be a partial output present.

CoherenceInput

Input for coherence metric.

Fields
metric_spec	CoherenceSpec Required. Spec for coherence score metric.
instance	CoherenceInstance Required. Coherence instance.

CoherenceInstance

Spec for coherence instance.

Fields
prediction	string Required. Output of the evaluated model.

CoherenceResult

Spec for coherence result.

Fields
explanation	string Output only. Explanation for coherence score.
score	float Output only. Coherence score.
confidence	float Output only. Confidence for coherence score.

CoherenceSpec

Spec for coherence score metric.

Fields
version	int32 Optional. Which version to use for evaluation.

ColabImage

Colab image of the runtime.

Fields
release_name	string Optional. The release name of the NotebookRuntime Colab image, e.g. "py310". If not specified, detault to the latest release.
description	string Output only. A human-readable description of the specified colab image release, populated by the system. Example: "Python 3.10", "Latest - current Python 3.11"

CometInput

Input for Comet metric.

Fields
metric_spec	CometSpec Required. Spec for comet metric.
instance	CometInstance Required. Comet instance.

CometInstance

Spec for Comet instance - The fields used for evaluation are dependent on the comet version.

Fields
prediction	string Required. Output of the evaluated model.
reference	string Optional. Ground truth used to compare against the prediction.
source	string Optional. Source text in original language.

CometResult

Spec for Comet result - calculates the comet score for the given instance using the version specified in the spec.

Fields
score	float Output only. Comet score. Range depends on version.

CometSpec

Spec for Comet metric.

Fields
source_language	string Optional. Source language in BCP-47 format.
target_language	string Optional. Target language in BCP-47 format. Covers both prediction and reference.
version	CometVersion Required. Which version to use for evaluation.

CometVersion

Comet version options.

Enums
COMET_VERSION_UNSPECIFIED	Comet version unspecified.
COMET_22_SRC_REF	Comet 22 for translation + source + reference (source-reference-combined).

CompleteTrialRequest

Request message for VizierService.CompleteTrial.

Fields
name	string Required. The Trial's name. Format: projects/{project}/locations/{location}/studies/{study}/trials/{trial}
final_measurement	Measurement Optional. If provided, it will be used as the completed Trial's final_measurement; Otherwise, the service will auto-select a previously reported measurement as the final-measurement
trial_infeasible	bool Optional. True if the Trial cannot be run with the given Parameter, and final_measurement will be ignored.
infeasible_reason	string Optional. A human readable reason why the trial was infeasible. This should only be provided if trial_infeasible is true.

CompletionStats

Success and error statistics of processing multiple entities (for example, DataItems or structured data rows) in batch.

Fields
successful_count	int64 Output only. The number of entities that had been processed successfully.
failed_count	int64 Output only. The number of entities for which any error was encountered.
incomplete_count	int64 Output only. In cases when enough errors are encountered a job, pipeline, or operation may be failed as a whole. Below is the number of entities for which the processing had not been finished (either in successful or failed state). Set to -1 if the number is unknown (for example, the operation failed before the total entity number could be collected).
successful_forecast_point_count	int64 Output only. The number of the successful forecast points that are generated by the forecasting model. This is ONLY used by the forecasting batch prediction.

ComputationBasedMetricSpec

Specification for a computation based metric.

Fields
type	ComputationBasedMetricType Required. The type of the computation based metric.
parameters	Struct Optional. A map of parameters for the metric, e.g. {"rouge_type": "rougeL"}.

ComputationBasedMetricType

Types of computation based metrics.

Enums
COMPUTATION_BASED_METRIC_TYPE_UNSPECIFIED	Unspecified computation based metric type.
EXACT_MATCH	Exact match metric.
BLEU	BLEU metric.
ROUGE	ROUGE metric.

ComputeTokensRequest

Request message for ComputeTokens RPC call.

Fields
endpoint	string Required. The name of the Endpoint requested to get lists of tokens and token ids.
instances[]	Value Optional. The instances that are the input to token computing API call. Schema is identical to the prediction schema of the text model, even for the non-text models, like chat models, or Codey models.
model	string Optional. The name of the publisher model requested to serve the prediction. Format: projects/{project}/locations/{location}/publishers/*/models/*
contents[]	Content Optional. Input content.

ComputeTokensResponse

Response message for ComputeTokens RPC call.

Fields
tokens_info[]	TokensInfo Lists of tokens info from the input. A ComputeTokensRequest could have multiple instances with a prompt in each instance. We also need to return lists of tokens info for the request with multiple instances.

ContainerRegistryDestination

The Artifact Registry location for the container image.

Fields
output_uri	string Required. Artifact Registry URI of a container image. Only Google Artifact Registry are supported now. Accepted forms: Google Artifact Registry path. For example: gcr.io/projectId/imageName:tag. Artifact Registry path. For example: us-central1-docker.pkg.dev/projectId/repoName/imageName:tag. If a tag is not specified, "latest" will be used as the default tag.

ContainerSpec

The spec of a Container.

Fields
image_uri	string Required. The URI of a container image in the Artifact Registry that is to be run on each worker replica.
command[]	string The command to be invoked when the container is started. It overrides the entrypoint instruction in Dockerfile when provided.
args[]	string The arguments to be passed when starting the container.
env[]	EnvVar Environment variables to be passed to the container. Maximum limit is 100.

Content

The structured data content of a message.

A Content message contains a role field, which indicates the producer of the content, and a parts field, which contains the multi-part data of the message.

Fields
role	string Optional. The producer of the content. Must be either 'user' or 'model'. If not set, the service will default to 'user'.
parts[]	Part Required. A list of Part objects that make up a single message. Parts of a message can have different MIME types. A Content message must have at least one Part.

ContentMap

Map of placeholder in metric prompt template to contents of model input.

Fields
values	map<string, Contents> Optional. Map of placeholder to contents.

Contents

Repeated Content type.

Fields
contents[]	Content Optional. Repeated contents.

ContentsExample

A single example of a conversation with the model.

Fields
contents[]	Content Required. The content of the conversation with the model that resulted in the expected output.
expected_contents[]	ExpectedContent Required. The expected output for the given contents. To represent multi-step reasoning, this is a repeated field that contains the iterative steps of the expected output.

ExpectedContent

A single step of the expected output.

Fields
content	Content Required. A single step's content.

Context

Instance of a general context.

Fields
name	string Immutable. The resource name of the Context.
display_name	string User provided display name of the Context. May be up to 128 Unicode characters.
etag	string An eTag used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map<string, string> The labels with user-defined metadata to organize your Contexts. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Context (System labels are excluded).
create_time	Timestamp Output only. Timestamp when this Context was created.
update_time	Timestamp Output only. Timestamp when this Context was last updated.
parent_contexts[]	string Output only. A list of resource names of Contexts that are parents of this Context. A Context may have at most 10 parent_contexts.
schema_title	string The title of the schema describing the metadata. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store.
schema_version	string The version of the schema in schema_name to use. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store.
metadata	Struct Properties of the Context. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB.
description	string Description of the Context

ConversationTurn

Represents a single turn/invocation in the conversation.

Fields
turn_id	string Optional. A unique identifier for the turn. Useful for referencing specific turns across systems.
events[]	AgentEvent Optional. The list of events that occurred during this turn.
turn_index	int32 Required. The 0-based index of the turn in the conversation sequence.

CopyModelOperationMetadata

Details of ModelService.CopyModel operation.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

CopyModelRequest

Request message for ModelService.CopyModel.

Fields
parent	string Required. The resource name of the Location into which to copy the Model. Format: projects/{project}/locations/{location}
source_model	string Required. The resource name of the Model to copy. That Model must be in the same Project. Format: projects/{project}/locations/{location}/models/{model}
encryption_spec	EncryptionSpec Customer-managed encryption key options. If this is set, then the Model copy will be encrypted with the provided encryption key.
custom_service_account	string Optional. The user-provided custom service account to use to do the copy model. If empty, Agent Platform Service Agent will be used to access resources needed to upload the model. This account must belong to the destination project where the model is copied to, i.e., the project specified in the parent field of this request and have the Agent Platform Service Agent role in the source project. Requires the user copying the Model to have the iam.serviceAccounts.actAs permission on this service account.
Union field destination_model. If both fields are unset, a new Model will be created with a generated ID. destination_model can be only one of the following:
model_id	string Optional. Copy source_model into a new Model with this ID. The ID will become the final component of the model resource name. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.
parent_model	string Optional. Specify this field to copy source_model into this existing Model as a new version. Format: projects/{project}/locations/{location}/models/{model}

CopyModelResponse

Response message of ModelService.CopyModel operation.

Fields
model	string The name of the copied Model resource. Format: projects/{project}/locations/{location}/models/{model}
model_version_id	string Output only. The version ID of the model that is copied.

CorpusStatus

RagCorpus status.

Fields
state	State Output only. RagCorpus life state.
error_status	string Output only. Only when the state field is ERROR.

State

RagCorpus life state.

Enums
UNKNOWN	This state is not supposed to happen.
INITIALIZED	RagCorpus resource entry is initialized, but hasn't done validation.
ACTIVE	RagCorpus is provisioned successfully and is ready to serve.
ERROR	RagCorpus is in a problematic situation. See error_message field for details.

CorroborateContentRequest

Request message for CorroborateContent.

Fields
parent	string Required. The resource name of the Location from which to corroborate text. The users must have permission to make a call in the project. Format: projects/{project}/locations/{location}.
facts[]	Fact Optional. Facts used to generate the text can also be used to corroborate the text.
parameters	Parameters Optional. Parameters that can be set to override default settings per request.
content	Content Optional. Input content to corroborate, only text format is supported for now.

Parameters

Parameters that can be overrided per request.

Fields
citation_threshold	double Optional. Only return claims with citation score larger than the threshold.

CorroborateContentResponse

Response message for CorroborateContent.

Fields
claims[]	Claim Claims that are extracted from the input content and facts that support the claims.
corroboration_score	float Confidence score of corroborating content. Value is [0,1] with 1 is the most confidence.

CountTokensRequest

Request message for PredictionService.CountTokens.

Fields
endpoint	string Required. The name of the Endpoint requested to perform token counting. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
model	string Optional. The name of the publisher model requested to serve the prediction. Format: projects/{project}/locations/{location}/publishers/*/models/*
instances[]	Value Optional. The instances that are the input to token counting call. Schema is identical to the prediction schema of the underlying model.
contents[]	Content Optional. Input content.
tools[]	Tool Optional. A list of Tools the model may use to generate the next response. A Tool is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model.
system_instruction	Content Optional. The user provided system instructions for the model. Note: only text should be used in parts and content in each part will be in a separate paragraph.
generation_config	GenerationConfig Optional. Generation config that the model will use to generate the response.

CountTokensResponse

Response message for PredictionService.CountTokens.

Fields
total_tokens	int32 The total number of tokens counted across all instances from the request.
total_billable_characters	int32 The total number of billable characters counted across all instances from the request.
prompt_tokens_details[]	ModalityTokenCount Output only. List of modalities that were processed in the request input.

CreateAgentOperationMetadata

Metadata associated with the AgentService.CreateAgent operation.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

CreateAgentRequest

Request message for AgentService.CreateAgent.

Fields
parent	string Required. The resource name of the location to create the agent in. Format: projects/{project}/locations/{location}.
agent	Agent Required. The agent to create.

CreateArtifactRequest

Request message for MetadataService.CreateArtifact.

Fields
parent	string Required. The resource name of the MetadataStore where the Artifact should be created. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}
artifact	Artifact Required. The Artifact to create.
artifact_id	string The {artifact} portion of the resource name with the format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact} If not provided, the Artifact's ID will be a UUID generated by the service. Must be 4-128 characters in length. Valid characters are /[a-z][0-9]-/. Must be unique across all Artifacts in the parent MetadataStore. (Otherwise the request will fail with ALREADY_EXISTS, or PERMISSION_DENIED if the caller can't view the preexisting Artifact.)

CreateBatchPredictionJobRequest

Request message for JobService.CreateBatchPredictionJob.

Fields
parent	string Required. The resource name of the Location to create the BatchPredictionJob in. Format: projects/{project}/locations/{location}
batch_prediction_job	BatchPredictionJob Required. The BatchPredictionJob to create.

CreateCachedContentRequest

Request message for GenAiCacheService.CreateCachedContent.

Fields
parent	string Required. The parent resource where the cached content will be created
cached_content	CachedContent Required. The cached content to create

CreateContextRequest

Request message for MetadataService.CreateContext.

Fields
parent	string Required. The resource name of the MetadataStore where the Context should be created. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}
context	Context Required. The Context to create.
context_id	string The {context} portion of the resource name with the format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/contexts/{context}. If not provided, the Context's ID will be a UUID generated by the service. Must be 4-128 characters in length. Valid characters are /[a-z][0-9]-/. Must be unique across all Contexts in the parent MetadataStore. (Otherwise the request will fail with ALREADY_EXISTS, or PERMISSION_DENIED if the caller can't view the preexisting Context.)

CreateCustomJobRequest

Request message for JobService.CreateCustomJob.

Fields
parent	string Required. The resource name of the Location to create the CustomJob in. Format: projects/{project}/locations/{location}
custom_job	CustomJob Required. The CustomJob to create.

CreateDatasetOperationMetadata

Runtime operation information for DatasetService.CreateDataset.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.

CreateDatasetRequest

Request message for DatasetService.CreateDataset.

Fields
parent	string Required. The resource name of the Location to create the Dataset in. Format: projects/{project}/locations/{location}
dataset	Dataset Required. The Dataset to create.

CreateDatasetVersionOperationMetadata

Runtime operation information for DatasetService.CreateDatasetVersion.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

CreateDatasetVersionRequest

Request message for DatasetService.CreateDatasetVersion.

Fields
parent	string Required. The name of the Dataset resource. Format: projects/{project}/locations/{location}/datasets/{dataset}
dataset_version	DatasetVersion Required. The version to be created. The same CMEK policies with the original Dataset will be applied the dataset version. So here we don't need to specify the EncryptionSpecType here.

CreateDeploymentResourcePoolOperationMetadata

Runtime operation information for CreateDeploymentResourcePool method.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.

CreateDeploymentResourcePoolRequest

Request message for CreateDeploymentResourcePool method.

Fields
parent	string Required. The parent location resource where this DeploymentResourcePool will be created. Format: projects/{project}/locations/{location}
deployment_resource_pool	DeploymentResourcePool Required. The DeploymentResourcePool to create.
deployment_resource_pool_id	string Required. The ID to use for the DeploymentResourcePool, which will become the final component of the DeploymentResourcePool's resource name. The maximum length is 63 characters, and valid characters are /^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/.

CreateEndpointOperationMetadata

Runtime operation information for EndpointService.CreateEndpoint.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.
deployment_stage	DeploymentStage Output only. The deployment stage of the model. Only populated if this CreateEndpoint request deploys a model at the same time.

CreateEndpointRequest

Request message for EndpointService.CreateEndpoint.

Fields
parent	string Required. The resource name of the Location to create the Endpoint in. Format: projects/{project}/locations/{location}
endpoint	Endpoint Required. The Endpoint to create.
endpoint_id	string Immutable. The ID to use for endpoint, which will become the final component of the endpoint resource name. If not provided, Agent Platform will generate a value for this ID. If the first character is a letter, this value may be up to 63 characters, and valid characters are [a-z0-9-]. The last character must be a letter or number. If the first character is a number, this value may be up to 9 characters, and valid characters are [0-9] with no leading zeros. When using HTTP/JSON, this field is populated based on a query string argument, such as ?endpoint_id=12345. This is the fallback for fields that are not included in either the URI or the body.

CreateEntityTypeOperationMetadata

Details of operations that perform create EntityType.

Fields
generic_metadata	GenericOperationMetadata Operation metadata for EntityType.

CreateEntityTypeRequest

Request message for FeaturestoreService.CreateEntityType.

Fields
parent	string Required. The resource name of the Featurestore to create EntityTypes. Format: projects/{project}/locations/{location}/featurestores/{featurestore}
entity_type	EntityType The EntityType to create.
entity_type_id	string Required. The ID to use for the EntityType, which will become the final component of the EntityType's resource name. This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within a featurestore.

CreateExampleStoreOperationMetadata

Details of ExampleStoreService.CreateExampleStore operation.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

CreateExampleStoreRequest

Request message for ExampleStoreService.CreateExampleStore.

Fields
parent	string Required. The resource name of the Location to create the ExampleStore in. Format: projects/{project}/locations/{location}
example_store	ExampleStore Required. The Example Store to be created.

CreateExecutionRequest

Request message for MetadataService.CreateExecution.

Fields
parent	string Required. The resource name of the MetadataStore where the Execution should be created. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}
execution	Execution Required. The Execution to create.
execution_id	string The {execution} portion of the resource name with the format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution} If not provided, the Execution's ID will be a UUID generated by the service. Must be 4-128 characters in length. Valid characters are /[a-z][0-9]-/. Must be unique across all Executions in the parent MetadataStore. (Otherwise the request will fail with ALREADY_EXISTS, or PERMISSION_DENIED if the caller can't view the preexisting Execution.)

CreateExtensionControllerOperationMetadata

Details of ExtensionControllerService.CreateExtensionController operation.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

CreateFeatureGroupOperationMetadata

Details of operations that perform create FeatureGroup.

Fields
generic_metadata	GenericOperationMetadata Operation metadata for FeatureGroup.

CreateFeatureGroupRequest

Request message for FeatureRegistryService.CreateFeatureGroup.

Fields
parent	string Required. The resource name of the Location to create FeatureGroups. Format: projects/{project}/locations/{location}
feature_group	FeatureGroup Required. The FeatureGroup to create.
feature_group_id	string Required. The ID to use for this FeatureGroup, which will become the final component of the FeatureGroup's resource name. This value may be up to 128 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within the project and location.

CreateFeatureMonitorJobRequest

Request message for [FeatureRegistryService.CreateFeatureMonitorJobRequest][].

Fields
parent	string Required. The resource name of FeatureMonitor to create FeatureMonitorJob. Format: projects/{project}/locations/{location}/featureGroups/{feature_group}/featureMonitors/{feature_monitor}
feature_monitor_job	FeatureMonitorJob Required. The Monitor to create.
feature_monitor_job_id	int64 Optional. Output only. System-generated ID for feature monitor job.

CreateFeatureMonitorOperationMetadata

Details of operations that perform create FeatureMonitor.

Fields
generic_metadata	GenericOperationMetadata Operation metadata for Feature.

CreateFeatureMonitorRequest

Request message for [FeatureRegistryService.CreateFeatureMonitorRequest][].

Fields
parent	string Required. The resource name of FeatureGroup to create FeatureMonitor. Format: projects/{project}/locations/{location}/featureGroups/{featuregroup}
feature_monitor	FeatureMonitor Required. The Monitor to create.
feature_monitor_id	string Required. The ID to use for this FeatureMonitor, which will become the final component of the FeatureGroup's resource name. This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within the FeatureGroup.

CreateFeatureOnlineStoreOperationMetadata

Details of operations that perform create FeatureOnlineStore.

Fields
generic_metadata	GenericOperationMetadata Operation metadata for FeatureOnlineStore.

CreateFeatureOnlineStoreRequest

Request message for FeatureOnlineStoreAdminService.CreateFeatureOnlineStore.

Fields
parent	string Required. The resource name of the Location to create FeatureOnlineStores. Format: projects/{project}/locations/{location}
feature_online_store	FeatureOnlineStore Required. The FeatureOnlineStore to create.
feature_online_store_id	string Required. The ID to use for this FeatureOnlineStore, which will become the final component of the FeatureOnlineStore's resource name. This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within the project and location.

CreateFeatureOperationMetadata

Details of operations that perform create Feature.

Fields
generic_metadata	GenericOperationMetadata Operation metadata for Feature.

CreateFeatureRequest

Request message for FeaturestoreService.CreateFeature. Request message for FeatureRegistryService.CreateFeature.

Fields
parent	string Required. The resource name of the EntityType or FeatureGroup to create a Feature. Format for entity_type as parent: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} Format for feature_group as parent: projects/{project}/locations/{location}/featureGroups/{feature_group}
feature	Feature Required. The Feature to create.
feature_id	string Required. The ID to use for the Feature, which will become the final component of the Feature's resource name. This value may be up to 128 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within an EntityType/FeatureGroup.

CreateFeatureViewOperationMetadata

Details of operations that perform create FeatureView.

Fields
generic_metadata	GenericOperationMetadata Operation metadata for FeatureView Create.

CreateFeatureViewRequest

Request message for FeatureOnlineStoreAdminService.CreateFeatureView.

Fields
parent	string Required. The resource name of the FeatureOnlineStore to create FeatureViews. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}
feature_view	FeatureView Required. The FeatureView to create.
feature_view_id	string Required. The ID to use for the FeatureView, which will become the final component of the FeatureView's resource name. This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within a FeatureOnlineStore.
run_sync_immediately	bool Immutable. If set to true, one on demand sync will be run immediately, regardless whether the FeatureView.sync_config is configured or not.

CreateFeaturestoreOperationMetadata

Details of operations that perform create Featurestore.

Fields
generic_metadata	GenericOperationMetadata Operation metadata for Featurestore.

CreateFeaturestoreRequest

Request message for FeaturestoreService.CreateFeaturestore.

Fields
parent	string Required. The resource name of the Location to create Featurestores. Format: projects/{project}/locations/{location}
featurestore	Featurestore Required. The Featurestore to create.
featurestore_id	string Required. The ID to use for this Featurestore, which will become the final component of the Featurestore's resource name. This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within the project and location.

CreateHyperparameterTuningJobRequest

Request message for JobService.CreateHyperparameterTuningJob.

Fields
parent	string Required. The resource name of the Location to create the HyperparameterTuningJob in. Format: projects/{project}/locations/{location}
hyperparameter_tuning_job	HyperparameterTuningJob Required. The HyperparameterTuningJob to create.

CreateIndexEndpointOperationMetadata

Runtime operation information for IndexEndpointService.CreateIndexEndpoint.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.

CreateIndexEndpointRequest

Request message for IndexEndpointService.CreateIndexEndpoint.

Fields
parent	string Required. The resource name of the Location to create the IndexEndpoint in. Format: projects/{project}/locations/{location}
index_endpoint	IndexEndpoint Required. The IndexEndpoint to create.

CreateIndexOperationMetadata

Runtime operation information for IndexService.CreateIndex.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.
nearest_neighbor_search_operation_metadata	NearestNeighborSearchOperationMetadata The operation metadata with regard to Matching Engine Index operation.

CreateIndexRequest

Request message for IndexService.CreateIndex.

Fields
parent	string Required. The resource name of the Location to create the Index in. Format: projects/{project}/locations/{location}
index	Index Required. The Index to create.

CreateMemoryOperationMetadata

Details of MemoryBankService.CreateMemory operation.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

CreateMemoryRequest

Request message for MemoryBankService.CreateMemory.

Fields
parent	string Required. The resource name of the ReasoningEngine to create the Memory under. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}
memory	Memory Required. The Memory to be created.
memory_id	string Optional. The user defined ID to use for memory, which will become the final component of the memory resource name. If not provided, Agent Platform will generate a value for this ID. This value may be up to 63 characters, and valid characters are [a-z0-9-]. The first character must be a letter, and the last character must be a letter or number.

CreateMetadataSchemaRequest

Request message for MetadataService.CreateMetadataSchema.

Fields
parent	string Required. The resource name of the MetadataStore where the MetadataSchema should be created. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}
metadata_schema	MetadataSchema Required. The MetadataSchema to create.
metadata_schema_id	string The {metadata_schema} portion of the resource name with the format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/metadataSchemas/{metadataschema} If not provided, the MetadataStore's ID will be a UUID generated by the service. Must be 4-128 characters in length. Valid characters are /[a-z][0-9]-/. Must be unique across all MetadataSchemas in the parent Location. (Otherwise the request will fail with ALREADY_EXISTS, or PERMISSION_DENIED if the caller can't view the preexisting MetadataSchema.)

CreateMetadataStoreOperationMetadata

Details of operations that perform MetadataService.CreateMetadataStore.

Fields
generic_metadata	GenericOperationMetadata Operation metadata for creating a MetadataStore.

CreateMetadataStoreRequest

Request message for MetadataService.CreateMetadataStore.

Fields
parent	string Required. The resource name of the Location where the MetadataStore should be created. Format: projects/{project}/locations/{location}/
metadata_store	MetadataStore Required. The MetadataStore to create.
metadata_store_id	string The {metadatastore} portion of the resource name with the format: projects/{project}/locations/{location}/metadataStores/{metadatastore} If not provided, the MetadataStore's ID will be a UUID generated by the service. Must be 4-128 characters in length. Valid characters are /[a-z][0-9]-/. Must be unique across all MetadataStores in the parent Location. (Otherwise the request will fail with ALREADY_EXISTS, or PERMISSION_DENIED if the caller can't view the preexisting MetadataStore.)

CreateModelDeploymentMonitoringJobRequest

Request message for JobService.CreateModelDeploymentMonitoringJob.

Fields
parent	string Required. The parent of the ModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}
model_deployment_monitoring_job	ModelDeploymentMonitoringJob Required. The ModelDeploymentMonitoringJob to create

CreateModelMonitorOperationMetadata

Runtime operation information for ModelMonitoringService.CreateModelMonitor.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.

CreateModelMonitorRequest

Request message for ModelMonitoringService.CreateModelMonitor.

Fields
parent	string Required. The resource name of the Location to create the ModelMonitor in. Format: projects/{project}/locations/{location}
model_monitor	ModelMonitor Required. The ModelMonitor to create.
model_monitor_id	string Optional. The ID to use for the Model Monitor, which will become the final component of the model monitor resource name. The maximum length is 63 characters, and valid characters are /^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/.

CreateModelMonitoringJobRequest

Request message for ModelMonitoringService.CreateModelMonitoringJob.

Fields
parent	string Required. The parent of the ModelMonitoringJob. Format: projects/{project}/locations/{location}/modelMoniitors/{model_monitor}
model_monitoring_job	ModelMonitoringJob Required. The ModelMonitoringJob to create
model_monitoring_job_id	string Optional. The ID to use for the Model Monitoring Job, which will become the final component of the model monitoring job resource name. The maximum length is 63 characters, and valid characters are /^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/.

CreateNotebookExecutionJobOperationMetadata

Metadata information for NotebookService.CreateNotebookExecutionJob.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.
progress_message	string A human-readable message that shows the intermediate progress details of NotebookRuntime.

CreateNotebookExecutionJobRequest

Request message for [NotebookService.CreateNotebookExecutionJob]

Fields
parent	string Required. The resource name of the Location to create the NotebookExecutionJob. Format: projects/{project}/locations/{location}
notebook_execution_job	NotebookExecutionJob Required. The NotebookExecutionJob to create.
notebook_execution_job_id	string Optional. User specified ID for the NotebookExecutionJob.

CreateNotebookRuntimeTemplateOperationMetadata

Metadata information for NotebookService.CreateNotebookRuntimeTemplate.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.

CreateNotebookRuntimeTemplateRequest

Request message for NotebookService.CreateNotebookRuntimeTemplate.

Fields
parent	string Required. The resource name of the Location to create the NotebookRuntimeTemplate. Format: projects/{project}/locations/{location}
notebook_runtime_template	NotebookRuntimeTemplate Required. The NotebookRuntimeTemplate to create.
notebook_runtime_template_id	string Optional. User specified ID for the notebook runtime template.

CreateOnlineEvaluatorOperationMetadata

Metadata for the CreateOnlineEvaluator operation.

Fields
generic_metadata	GenericOperationMetadata Common part of operation metadata.

CreateOnlineEvaluatorRequest

Request message for CreateOnlineEvaluator.

Fields
parent	string Required. The parent resource where the OnlineEvaluator will be created. Format: projects/{project}/locations/{location}.
online_evaluator	OnlineEvaluator Required. The OnlineEvaluator to create.

CreatePersistentResourceOperationMetadata

Details of operations that perform create PersistentResource.

Fields
generic_metadata	GenericOperationMetadata Operation metadata for PersistentResource.
progress_message	string Progress Message for Create LRO

CreatePersistentResourceRequest

Request message for PersistentResourceService.CreatePersistentResource.

Fields
parent	string Required. The resource name of the Location to create the PersistentResource in. Format: projects/{project}/locations/{location}
persistent_resource	PersistentResource Required. The PersistentResource to create.
persistent_resource_id	string Required. The ID to use for the PersistentResource, which become the final component of the PersistentResource's resource name. The maximum length is 63 characters, and valid characters are /^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$/.

CreatePipelineJobRequest

Request message for PipelineService.CreatePipelineJob.

Fields
parent	string Required. The resource name of the Location to create the PipelineJob in. Format: projects/{project}/locations/{location}
pipeline_job	PipelineJob Required. The PipelineJob to create.
pipeline_job_id	string The ID to use for the PipelineJob, which will become the final component of the PipelineJob name. If not provided, an ID will be automatically generated. This value should be less than 128 characters, and valid characters are /[a-z][0-9]-/.

CreateRagCorpusOperationMetadata

Runtime operation information for VertexRagDataService.CreateRagCorpus.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.

CreateRagCorpusRequest

Request message for VertexRagDataService.CreateRagCorpus.

Fields
parent	string Required. The resource name of the Location to create the RagCorpus in. Format: projects/{project}/locations/{location}
rag_corpus	RagCorpus Required. The RagCorpus to create.

CreateRagDataSchemaRequest

Request message for VertexRagDataService.CreateRagDataSchema.

Fields
parent	string Required. The resource name of the RagCorpus to create the RagDataSchema in. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}
rag_data_schema	RagDataSchema Required. The RagDataSchema to create.
rag_data_schema_id	string Optional. The ID to use for the RagDataSchema, which will become the final component of the RagDataSchema's resource name if the user chooses to specify. Otherwise, RagDataSchema id will be generated by system. This value should be up to 63 characters, and valid characters are /[a-z][0-9]-/. The first character must be a letter, the last could be a letter or a number.

CreateRagMetadataRequest

Request message for CreateRagMetadata.

Fields
parent	string Required. The parent resource where this metadata will be created. Format: projects/{project_number}/locations/{location_id}/ragCorpora/{rag_corpus}/ragFiles/{rag_file}
rag_metadata	RagMetadata Required. The metadata to create.
rag_metadata_id	string Optional. The ID to use for the metadata, which will become the final component of the metadata's resource name if the user chooses to specify. Otherwise, metadata id will be generated by system. This value should be up to 63 characters, and valid characters are /[a-z][0-9]-/. The first character must be a letter, the last could be a letter or a number.

CreateReasoningEngineOperationMetadata

Details of ReasoningEngineService.CreateReasoningEngine operation.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

CreateReasoningEngineRequest

Request message for ReasoningEngineService.CreateReasoningEngine.

Fields
parent	string Required. The resource name of the Location to create the ReasoningEngine in. Format: projects/{project}/locations/{location}
reasoning_engine	ReasoningEngine Required. The ReasoningEngine to create.

CreateRegistryFeatureOperationMetadata

Details of operations that perform create FeatureGroup.

Fields
generic_metadata	GenericOperationMetadata Operation metadata for Feature.

CreateScheduleRequest

Request message for ScheduleService.CreateSchedule.

Fields
parent	string Required. The resource name of the Location to create the Schedule in. Format: projects/{project}/locations/{location}
schedule	Schedule Required. The Schedule to create.

CreateSessionOperationMetadata

Metadata associated with the SessionService.CreateSession operation.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

CreateSessionRequest

Request message for SessionService.CreateSession.

Fields
parent	string Required. The resource name of the location to create the session in. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}
session	Session Required. The session to create.
session_id	string Optional. The user defined ID to use for session, which will become the final component of the session resource name. If not provided, Agent Platform will generate a value for this ID. This value may be up to 63 characters, and valid characters are [a-z0-9-]. The first and last characters must be a letter or number.

CreateSkillOperationMetadata

Details of SkillRegistryService.CreateSkill operation.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

CreateSkillRequest

Request message for SkillRegistryService.CreateSkill.

Fields
parent	string Required. The location to create the Skill in. Format: projects/{project}/locations/{location}
skill	Skill Required. The Skill to be created.
skill_id	string Required. The ID to use for the Skill, which will become the final component of the Skill's resource name. If not provided, a system-generated ID will be used. This value must be 1-63 characters. Valid characters are lowercase letters, numbers, and hyphens. The first character must be a lowercase letter, and the last character must be a lowercase letter or a number. Specifically, the ID must match the regular expression: ^[a-z]([a-z0-9-]{0,61}[a-z0-9])?$ See AIP-122 for more details.

CreateSolverOperationMetadata

Runtime operation information for SolverService.CreateSolver.

Fields
generic_metadata	GenericOperationMetadata The generic operation information.

CreateSpecialistPoolOperationMetadata

Runtime operation information for SpecialistPoolService.CreateSpecialistPool.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.

CreateSpecialistPoolRequest

Request message for SpecialistPoolService.CreateSpecialistPool.

Fields
parent	string Required. The parent Project name for the new SpecialistPool. The form is projects/{project}/locations/{location}.
specialist_pool	SpecialistPool Required. The SpecialistPool to create.

CreateStudyRequest

Request message for VizierService.CreateStudy.

Fields
parent	string Required. The resource name of the Location to create the CustomJob in. Format: projects/{project}/locations/{location}
study	Study Required. The Study configuration used to create the Study.

CreateTensorboardExperimentRequest

Request message for TensorboardService.CreateTensorboardExperiment.

Fields
parent	string Required. The resource name of the Tensorboard to create the TensorboardExperiment in. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
tensorboard_experiment	TensorboardExperiment The TensorboardExperiment to create.
tensorboard_experiment_id	string Required. The ID to use for the Tensorboard experiment, which becomes the final component of the Tensorboard experiment's resource name. This value should be 1-128 characters, and valid characters are /[a-z][0-9]-/.

CreateTensorboardOperationMetadata

Details of operations that perform create Tensorboard.

Fields
generic_metadata	GenericOperationMetadata Operation metadata for Tensorboard.

CreateTensorboardRequest

Request message for TensorboardService.CreateTensorboard.

Fields
parent	string Required. The resource name of the Location to create the Tensorboard in. Format: projects/{project}/locations/{location}
tensorboard	Tensorboard Required. The Tensorboard to create.

CreateTensorboardRunRequest

Request message for TensorboardService.CreateTensorboardRun.

Fields
parent	string Required. The resource name of the TensorboardExperiment to create the TensorboardRun in. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}
tensorboard_run	TensorboardRun Required. The TensorboardRun to create.
tensorboard_run_id	string Required. The ID to use for the Tensorboard run, which becomes the final component of the Tensorboard run's resource name. This value should be 1-128 characters, and valid characters are /[a-z][0-9]-/.

CreateTensorboardTimeSeriesRequest

Request message for TensorboardService.CreateTensorboardTimeSeries.

Fields
parent	string Required. The resource name of the TensorboardRun to create the TensorboardTimeSeries in. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}
tensorboard_time_series_id	string Optional. The user specified unique ID to use for the TensorboardTimeSeries, which becomes the final component of the TensorboardTimeSeries's resource name. This value should match "[a-z0-9][a-z0-9-]{0, 127}"
tensorboard_time_series	TensorboardTimeSeries Required. The TensorboardTimeSeries to create.

CreateTrainingPipelineRequest

Request message for PipelineService.CreateTrainingPipeline.

Fields
parent	string Required. The resource name of the Location to create the TrainingPipeline in. Format: projects/{project}/locations/{location}
training_pipeline	TrainingPipeline Required. The TrainingPipeline to create.

CreateTrialRequest

Request message for VizierService.CreateTrial.

Fields
parent	string Required. The resource name of the Study to create the Trial in. Format: projects/{project}/locations/{location}/studies/{study}
trial	Trial Required. The Trial to create.

CreateTuningJobRequest

Request message for GenAiTuningService.CreateTuningJob.

Fields
parent	string Required. The resource name of the location to create the tuning job in. Format: projects/{project}/locations/{location}
tuning_job	TuningJob Required. The tuning job to create.

CsvDestination

The storage details for CSV output content.

Fields
gcs_destination	GcsDestination Required. Google Cloud Storage location.

CsvSource

The storage details for CSV input content.

Fields
gcs_source	GcsSource Required. Google Cloud Storage location.

CustomCodeExecutionResult

Result for custom code execution metric.

Fields
score	float Output only. Custom code execution score.

CustomCodeExecutionSpec

Specificies a metric that is populated by evaluating user-defined Python code.

Fields

evaluation_function

string Required. Python function. Expected user to define the following function, e.g.: def evaluate(instance: dict[str, Any]) -> float: Please include this function signature in the code snippet. Instance is the evaluation instance, any fields populated in the instance are available to the function as instance[field_name]. Example: Example input: instance= EvaluationInstance( response=EvaluationInstance.InstanceData(text="The answer is 4."), reference=EvaluationInstance.InstanceData(text="4") ) Example converted input: { 'response': {'text': 'The answer is 4.'}, 'reference': {'text': '4'} } Example python function: def evaluate(instance: dict[str, Any]) -> float: if instance['response']['text'] == instance['reference']['text']: return 1.0 return 0.0 CustomCodeExecutionSpec is also supported in Batch Evaluation (EvalDataset RPC) and Tuning Evaluation. Each line in the input jsonl file will be converted to dict[str, Any] and passed to the evaluation function.

CustomJob

Represents a job that runs custom workloads such as a Docker container or a Python package. A CustomJob can have multiple worker pools and each worker pool can have its own machine and input spec. A CustomJob will be cleaned up once the job enters terminal state (failed or succeeded).

Fields
name	string Output only. Resource name of a CustomJob.
display_name	string Required. The display name of the CustomJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
job_spec	CustomJobSpec Required. Job spec.
state	JobState Output only. The detailed state of the job.
create_time	Timestamp Output only. Time when the CustomJob was created.
start_time	Timestamp Output only. Time when the CustomJob for the first time entered the JOB_STATE_RUNNING state.
end_time	Timestamp Output only. Time when the CustomJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
update_time	Timestamp Output only. Time when the CustomJob was most recently updated.
error	Status Output only. Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
labels	map<string, string> The labels with user-defined metadata to organize CustomJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
encryption_spec	EncryptionSpec Customer-managed encryption key options for a CustomJob. If this is set, then all resources created by the CustomJob will be encrypted with the provided encryption key.
web_access_uris	map<string, string> Output only. URIs for accessing interactive shells (one URI for each training node). Only available if job_spec.enable_web_access is true. The keys are names of each node in the training job; for example, workerpool0-0 for the primary node, workerpool1-0 for the first node in the second worker pool, and workerpool1-1 for the second node in the second worker pool. The values are the URIs for each node's interactive shell.
satisfies_pzs	bool Output only. Reserved for future use.
satisfies_pzi	bool Output only. Reserved for future use.

CustomJobSpec

Represents the spec of a CustomJob.

Fields
persistent_resource_id	string Optional. The ID of the PersistentResource in the same Project and Location which to run If this is specified, the job will be run on existing machines held by the PersistentResource instead of on-demand short-live machines. The network and CMEK configs on the job should be consistent with those on the PersistentResource, otherwise, the job will be rejected.
worker_pool_specs[]	WorkerPoolSpec Required. The spec of the worker pools including machine type and Docker image. All worker pools except the first one are optional and can be skipped by providing an empty value.
scheduling	Scheduling Scheduling options for a CustomJob.
service_account	string Specifies the service account for workload run-as account. Users submitting jobs must have act-as permission on this run-as account. If unspecified, the Agent Platform Custom Code Service Agent for the CustomJob's project is used.
network	string Optional. The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. To specify this field, you must have already configured VPC Network Peering for Agent Platform. If this field is left unspecified, the job is not peered with any network.
reserved_ip_ranges[]	string Optional. A list of names for the reserved ip ranges under the VPC network that can be used for this job. If set, we will deploy the job within the provided ip ranges. Otherwise, the job will be deployed to any ip ranges under the provided VPC network. Example: ['vertex-ai-ip-range'].
psc_interface_config	PscInterfaceConfig Optional. Configuration for PSC-I for CustomJob.
base_output_directory	GcsDestination The Cloud Storage location to store the output of this CustomJob or HyperparameterTuningJob. For HyperparameterTuningJob, the baseOutputDirectory of each child CustomJob backing a Trial is set to a subdirectory of name id under its parent HyperparameterTuningJob's baseOutputDirectory. The following Agent Platform environment variables will be passed to containers or python modules when this field is set: For CustomJob: AIP_MODEL_DIR = <base_output_directory>/model/ AIP_CHECKPOINT_DIR = <base_output_directory>/checkpoints/ AIP_TENSORBOARD_LOG_DIR = <base_output_directory>/logs/ For CustomJob backing a Trial of HyperparameterTuningJob: AIP_MODEL_DIR = <base_output_directory>/<trial_id>/model/ AIP_CHECKPOINT_DIR = <base_output_directory>/<trial_id>/checkpoints/ AIP_TENSORBOARD_LOG_DIR = <base_output_directory>/<trial_id>/logs/
protected_artifact_location_id	string The ID of the location to store protected artifacts. e.g. us-central1. Populate only when the location is different than CustomJob location. List of supported locations: https://cloud.google.com/vertex-ai/docs/general/locations
tensorboard	string Optional. The name of a Agent Platform Tensorboard resource to which this CustomJob will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
enable_web_access	bool Optional. Whether you want Agent Platform to enable interactive shell access to training containers. If set to true, you can access interactive shells at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
enable_dashboard_access	bool Optional. Whether you want Agent Platform to enable access to the customized dashboard in training chief container. If set to true, you can access the dashboard at the URIs given by CustomJob.web_access_uris or Trial.web_access_uris (within HyperparameterTuningJob.trials).
experiment	string Optional. The Experiment associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}
experiment_run	string Optional. The Experiment Run associated with this job. Format: projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}
models[]	string Optional. The name of the Model resources for which to generate a mapping to artifact URIs. Applicable only to some of the Google-provided custom jobs. Format: projects/{project}/locations/{location}/models/{model} In order to retrieve a specific version of the model, also provide the version ID or version alias. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden If no version ID or alias is specified, the "default" version will be returned. The "default" version alias is created for the first version of the model, and can be moved to other versions later on. There will be exactly one default version.

CustomOutput

Spec for custom output.

Fields
Union field custom_output. Custom output. custom_output can be only one of the following:
raw_outputs	RawOutput Output only. List of raw output strings.

CustomOutputFormatConfig

Spec for custom output format configuration.

Fields
Union field custom_output_format_config. Custom output format configuration. custom_output_format_config can be only one of the following:
return_raw_output	bool Optional. Whether to return raw output.

DataItem

A piece of data in a Dataset. Could be an image, a video, a document or plain text.

Fields
name	string Output only. The resource name of the DataItem.
create_time	Timestamp Output only. Timestamp when this DataItem was created.
update_time	Timestamp Output only. Timestamp when this DataItem was last updated.
labels	map<string, string> Optional. The labels with user-defined metadata to organize your DataItems. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one DataItem(System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
payload	Value Required. The data that the DataItem represents (for example, an image or a text snippet). The schema of the payload is stored in the parent Dataset's metadata schema's dataItemSchemaUri field.
etag	string Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
satisfies_pzs	bool Output only. Reserved for future use.
satisfies_pzi	bool Output only. Reserved for future use.

DataItemView

A container for a single DataItem and Annotations on it.

Fields
data_item	DataItem The DataItem.
annotations[]	Annotation The Annotations on the DataItem. If too many Annotations should be returned for the DataItem, this field will be truncated per annotations_limit in request. If it was, then the has_truncated_annotations will be set to true.
has_truncated_annotations	bool True if and only if the Annotations field has been truncated. It happens if more Annotations for this DataItem met the request's annotation_filter than are allowed to be returned by annotations_limit. Note that if Annotations field is not being returned due to field mask, then this field will not be set to true no matter how many Annotations are there.

Dataset

A collection of DataItems and Annotations on them.

Fields
name	string Output only. Identifier. The resource name of the Dataset. Format: projects/{project}/locations/{location}/datasets/{dataset}
display_name	string Required. The user-defined name of the Dataset. The name can be up to 128 characters long and can consist of any UTF-8 characters.
description	string The description of the Dataset.
metadata_schema_uri	string Required. Points to a YAML file stored on Google Cloud Storage describing additional information about the Dataset. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/metadata/.
metadata	Value Required. Additional information about the Dataset.
data_item_count	int64 Output only. The number of DataItems in this Dataset. Only apply for non-structured Dataset.
create_time	Timestamp Output only. Timestamp when this Dataset was created.
update_time	Timestamp Output only. Timestamp when this Dataset was last updated.
etag	string Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map<string, string> The labels with user-defined metadata to organize your Datasets. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Dataset (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Dataset: "aiplatform.googleapis.com/dataset_metadata_schema": output only, its value is the metadata_schema's title.
saved_queries[]	SavedQuery All SavedQueries belong to the Dataset will be returned in List/Get Dataset response. The annotation_specs field will not be populated except for UI cases which will only use annotation_spec_count. In CreateDataset request, a SavedQuery is created together if this field is set, up to one SavedQuery can be set in CreateDatasetRequest. The SavedQuery should not contain any AnnotationSpec.
encryption_spec	EncryptionSpec Customer-managed encryption key spec for a Dataset. If set, this Dataset and all sub-resources of this Dataset will be secured by this key.
metadata_artifact	string Output only. The resource name of the Artifact that was created in MetadataStore when creating the Dataset. The Artifact resource name pattern is projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}.
model_reference	string Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets.
satisfies_pzs	bool Output only. Reserved for future use.
satisfies_pzi	bool Output only. Reserved for future use.

DatasetDistribution

Distribution computed over a tuning dataset.

Fields
sum	double Output only. Sum of a given population of values.
min	double Output only. The minimum of the population values.
max	double Output only. The maximum of the population values.
mean	double Output only. The arithmetic mean of the values in the population.
median	double Output only. The median of the values in the population.
p5	double Output only. The 5th percentile of the values in the population.
p95	double Output only. The 95th percentile of the values in the population.
buckets[]	DistributionBucket Output only. Defines the histogram bucket.

DistributionBucket

Dataset bucket used to create a histogram for the distribution given a population of values.

Fields
count	int64 Output only. Number of values in the bucket.
left	double Output only. Left bound of the bucket.
right	double Output only. Right bound of the bucket.

DatasetStats

Statistics computed over a tuning dataset.

Fields
tuning_dataset_example_count	int64 Output only. Number of examples in the tuning dataset.
total_billable_token_count	int64 Output only. Number of billable tokens in the tuning dataset.
total_tuning_character_count	int64 Output only. Number of tuning characters in the tuning dataset.
total_billable_character_count	int64 Output only. Number of billable characters in the tuning dataset.
tuning_step_count	int64 Output only. Number of tuning steps for this Tuning Job.
user_input_token_distribution	DatasetDistribution Output only. Dataset distributions for the user input tokens.
user_message_per_example_distribution	DatasetDistribution Output only. Dataset distributions for the messages per example.
user_dataset_examples[]	Content Output only. Sample user messages in the training dataset uri.
dropped_example_indices[]	int64 Output only. A partial sample of the indices (starting from 1) of the dropped examples.
dropped_example_reasons[]	string Output only. For each index in dropped_example_indices, the user-facing reason why the example was dropped.
user_output_token_distribution	DatasetDistribution Output only. Dataset distributions for the user output tokens.

DatasetVersion

Describes the dataset version.

Fields
name	string Output only. Identifier. The resource name of the DatasetVersion. Format: projects/{project}/locations/{location}/datasets/{dataset}/datasetVersions/{dataset_version}
create_time	Timestamp Output only. Timestamp when this DatasetVersion was created.
update_time	Timestamp Output only. Timestamp when this DatasetVersion was last updated.
etag	string Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
big_query_dataset_name	string Output only. Name of the associated BigQuery dataset.
display_name	string The user-defined name of the DatasetVersion. The name can be up to 128 characters long and can consist of any UTF-8 characters.
metadata	Value Required. Output only. Additional information about the DatasetVersion.
model_reference	string Output only. Reference to the public base model last used by the dataset version. Only set for prompt dataset versions.
satisfies_pzs	bool Output only. Reserved for future use.
satisfies_pzi	bool Output only. Reserved for future use.

DedicatedResources

A description of resources that are dedicated to a DeployedModel or DeployedIndex, and that need a higher degree of manual configuration.

Fields
machine_spec	MachineSpec Required. Immutable. The specification of a single machine being used.
min_replica_count	int32 Required. Immutable. The minimum number of machine replicas that will be always deployed on. This value must be greater than or equal to 1. If traffic increases, it may dynamically be deployed onto more replicas, and as traffic decreases, some of these extra replicas may be freed.
max_replica_count	int32 Immutable. The maximum number of replicas that may be deployed on when the traffic against it increases. If the requested value is too large, the deployment will error, but if deployment succeeds then the ability to scale to that many replicas is guaranteed (barring service outages). If traffic increases beyond what its replicas at maximum may handle, a portion of the traffic will be dropped. If this value is not provided, will use min_replica_count as the default value. The value of this field impacts the charge against Agent Platform CPU and GPU quotas. Specifically, you will be charged for (max_replica_count * number of cores in the selected machine type) and (max_replica_count * number of GPUs per replica in the selected machine type).
required_replica_count	int32 Optional. Number of required available replicas for the deployment to succeed. This field is only needed when partial deployment/mutation is desired. If set, the deploy/mutate operation will succeed once available_replica_count reaches required_replica_count, and the rest of the replicas will be retried. If not set, the default required_replica_count will be min_replica_count.
initial_replica_count	int32 Immutable. Number of initial replicas being deployed on when scaling the workload up from zero or when creating the workload in case min_replica_count = 0. When min_replica_count > 0 (meaning that the scale-to-zero feature is not enabled), initial_replica_count should not be set. When min_replica_count = 0 (meaning that the scale-to-zero feature is enabled), initial_replica_count should be larger than zero, but no greater than max_replica_count.
autoscaling_metric_specs[]	AutoscalingMetricSpec Immutable. The metric specifications that overrides a resource utilization metric (CPU utilization, accelerator's duty cycle, and so on) target value (default to 60 if not set). At most one entry is allowed per metric. If machine_spec.accelerator_count is above 0, the autoscaling will be based on both CPU utilization and accelerator's duty cycle metrics and scale up when either metrics exceeds its target value while scale down if both metrics are under their target value. The default target value is 60 for both metrics. If machine_spec.accelerator_count is 0, the autoscaling will be based on CPU utilization metric only with default target value 60 if not explicitly set. For example, in the case of Online Prediction, if you want to override target CPU utilization to 80, you should set autoscaling_metric_specs.metric_name to aiplatform.googleapis.com/prediction/online/cpu/utilization and autoscaling_metric_specs.target to 80.
spot	bool Optional. If true, schedule the deployment workload on spot VMs.
flex_start	FlexStart Optional. Immutable. If set, use DWS resource to schedule the deployment workload. reference: (https://cloud.google.com/blog/products/compute/introducing-dynamic-workload-scheduler)
scale_to_zero_spec	ScaleToZeroSpec Optional. Specification for scale-to-zero feature.

ScaleToZeroSpec

Specification for scale-to-zero feature.

Fields
min_scaleup_period	Duration Optional. Minimum duration that a deployment will be scaled up before traffic is evaluated for potential scale-down. [MinValue=300] (5 minutes) [MaxValue=28800] (8 hours)
idle_scaledown_period	Duration Optional. Duration of no traffic before scaling to zero. [MinValue=300] (5 minutes) [MaxValue=28800] (8 hours)

DeleteAgentRequest

Request message for AgentService.DeleteAgent.

Fields
name	string Required. The resource name of the agent to delete. Format: projects/{project}/locations/{location}/agents/{agent}.

DeleteArtifactRequest

Request message for MetadataService.DeleteArtifact.

Fields
name	string Required. The resource name of the Artifact to delete. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact}
etag	string Optional. The etag of the Artifact to delete. If this is provided, it must match the server's etag. Otherwise, the request will fail with a FAILED_PRECONDITION.

DeleteBatchPredictionJobRequest

Request message for JobService.DeleteBatchPredictionJob.

Fields
name	string Required. The name of the BatchPredictionJob resource to be deleted. Format: projects/{project}/locations/{location}/batchPredictionJobs/{batch_prediction_job}

DeleteCachedContentRequest

Request message for GenAiCacheService.DeleteCachedContent.

Fields
name	string Required. The resource name referring to the cached content

DeleteContextRequest

Request message for MetadataService.DeleteContext.

Fields
name	string Required. The resource name of the Context to delete. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/contexts/{context}
force	bool The force deletion semantics is still undefined. Users should not use this field.
etag	string Optional. The etag of the Context to delete. If this is provided, it must match the server's etag. Otherwise, the request will fail with a FAILED_PRECONDITION.

DeleteCustomJobRequest

Request message for JobService.DeleteCustomJob.

Fields
name	string Required. The name of the CustomJob resource to be deleted. Format: projects/{project}/locations/{location}/customJobs/{custom_job}

DeleteDatasetRequest

Request message for DatasetService.DeleteDataset.

Fields
name	string Required. The resource name of the Dataset to delete. Format: projects/{project}/locations/{location}/datasets/{dataset}

DeleteDatasetVersionRequest

Request message for DatasetService.DeleteDatasetVersion.

Fields
name	string Required. The resource name of the Dataset version to delete. Format: projects/{project}/locations/{location}/datasets/{dataset}/datasetVersions/{dataset_version}

DeleteDeploymentResourcePoolRequest

Request message for DeleteDeploymentResourcePool method.

Fields
name	string Required. The name of the DeploymentResourcePool to delete. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

DeleteEndpointRequest

Request message for EndpointService.DeleteEndpoint.

Fields
name	string Required. The name of the Endpoint resource to be deleted. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

DeleteEntityTypeRequest

Request message for FeaturestoreService.DeleteEntityType.

Fields
name	string Required. The name of the EntityType to be deleted. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}
force	bool If set to true, any Features for this EntityType will also be deleted. (Otherwise, the request will only work if the EntityType has no Features.)

DeleteExampleStoreOperationMetadata

Details of ExampleStoreService.DeleteExampleStore operation.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

DeleteExampleStoreRequest

Request message for ExampleStoreService.DeleteExampleStore.

Fields
name	string Required. The resource name of the ExampleStore to be deleted. Format: projects/{project}/locations/{location}/exampleStores/{example_store}

DeleteExecutionRequest

Request message for MetadataService.DeleteExecution.

Fields
name	string Required. The resource name of the Execution to delete. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution}
etag	string Optional. The etag of the Execution to delete. If this is provided, it must match the server's etag. Otherwise, the request will fail with a FAILED_PRECONDITION.

DeleteExtensionRequest

Request message for ExtensionRegistryService.DeleteExtension.

Fields
name	string Required. The name of the Extension resource to be deleted. Format: projects/{project}/locations/{location}/extensions/{extension}

DeleteFeatureGroupRequest

Request message for FeatureRegistryService.DeleteFeatureGroup.

Fields
name	string Required. The name of the FeatureGroup to be deleted. Format: projects/{project}/locations/{location}/featureGroups/{feature_group}
force	bool If set to true, any Features under this FeatureGroup will also be deleted. (Otherwise, the request will only work if the FeatureGroup has no Features.)

DeleteFeatureMonitorRequest

Request message for FeatureRegistryService.DeleteFeatureMonitor.

Fields
name	string Required. The name of the FeatureMonitor to be deleted. Format: projects/{project}/locations/{location}/featureGroups/{feature_group}/featureMonitors/{feature_monitor}

DeleteFeatureOnlineStoreRequest

Request message for FeatureOnlineStoreAdminService.DeleteFeatureOnlineStore.

Fields
name	string Required. The name of the FeatureOnlineStore to be deleted. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}
force	bool If set to true, any FeatureViews and Features for this FeatureOnlineStore will also be deleted. (Otherwise, the request will only work if the FeatureOnlineStore has no FeatureViews.)

DeleteFeatureRequest

Request message for FeaturestoreService.DeleteFeature. Request message for FeatureRegistryService.DeleteFeature.

Fields
name	string Required. The name of the Features to be deleted. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature}

DeleteFeatureValuesOperationMetadata

Details of operations that delete Feature values.

Fields
generic_metadata	GenericOperationMetadata Operation metadata for Featurestore delete Features values.

DeleteFeatureValuesRequest

Request message for FeaturestoreService.DeleteFeatureValues.

Fields
entity_type	string Required. The resource name of the EntityType grouping the Features for which values are being deleted from. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entityType}
Union field DeleteOption. Defines options to select feature values to be deleted. DeleteOption can be only one of the following:
select_entity	SelectEntity Select feature values to be deleted by specifying entities.
select_time_range_and_feature	SelectTimeRangeAndFeature Select feature values to be deleted by specifying time range and features.

SelectEntity

Message to select entity. If an entity id is selected, all the feature values corresponding to the entity id will be deleted, including the entityId.

Fields
entity_id_selector	EntityIdSelector Required. Selectors choosing feature values of which entity id to be deleted from the EntityType.

SelectTimeRangeAndFeature

Message to select time range and feature. Values of the selected feature generated within an inclusive time range will be deleted. Using this option permanently deletes the feature values from the specified feature IDs within the specified time range. This might include data from the online storage. If you want to retain any deleted historical data in the online storage, you must re-ingest it.

Fields
time_range	Interval Required. Select feature generated within a half-inclusive time range. The time range is lower inclusive and upper exclusive.
feature_selector	FeatureSelector Required. Selectors choosing which feature values to be deleted from the EntityType.
skip_online_storage_delete	bool If set, data will not be deleted from online storage. When time range is older than the data in online storage, setting this to be true will make the deletion have no impact on online serving.

DeleteFeatureValuesResponse

Response message for FeaturestoreService.DeleteFeatureValues.

Fields
Union field response. Response based on which delete option is specified in the request response can be only one of the following:
select_entity	SelectEntity Response for request specifying the entities to delete
select_time_range_and_feature	SelectTimeRangeAndFeature Response for request specifying time range and feature

SelectEntity

Response message if the request uses the SelectEntity option.

Fields
offline_storage_deleted_entity_row_count	int64 The count of deleted entity rows in the offline storage. Each row corresponds to the combination of an entity ID and a timestamp. One entity ID can have multiple rows in the offline storage.
online_storage_deleted_entity_count	int64 The count of deleted entities in the online storage. Each entity ID corresponds to one entity.

SelectTimeRangeAndFeature

Response message if the request uses the SelectTimeRangeAndFeature option.

Fields
impacted_feature_count	int64 The count of the features or columns impacted. This is the same as the feature count in the request.
offline_storage_modified_entity_row_count	int64 The count of modified entity rows in the offline storage. Each row corresponds to the combination of an entity ID and a timestamp. One entity ID can have multiple rows in the offline storage. Within each row, only the features specified in the request are deleted.
online_storage_modified_entity_count	int64 The count of modified entities in the online storage. Each entity ID corresponds to one entity. Within each entity, only the features specified in the request are deleted.

DeleteFeatureViewRequest

Request message for FeatureOnlineStoreAdminService.DeleteFeatureView.

Fields
name	string Required. The name of the FeatureView to be deleted. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}

DeleteFeaturestoreRequest

Request message for FeaturestoreService.DeleteFeaturestore.

Fields
name	string Required. The name of the Featurestore to be deleted. Format: projects/{project}/locations/{location}/featurestores/{featurestore}
force	bool If set to true, any EntityTypes and Features for this Featurestore will also be deleted. (Otherwise, the request will only work if the Featurestore has no EntityTypes.)

DeleteHyperparameterTuningJobRequest

Request message for JobService.DeleteHyperparameterTuningJob.

Fields
name	string Required. The name of the HyperparameterTuningJob resource to be deleted. Format: projects/{project}/locations/{location}/hyperparameterTuningJobs/{hyperparameter_tuning_job}

DeleteIndexEndpointRequest

Request message for IndexEndpointService.DeleteIndexEndpoint.

Fields
name	string Required. The name of the IndexEndpoint resource to be deleted. Format: projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}

DeleteIndexRequest

Request message for IndexService.DeleteIndex.

Fields
name	string Required. The name of the Index resource to be deleted. Format: projects/{project}/locations/{location}/indexes/{index}

DeleteMemoryOperationMetadata

Details of MemoryBankService.DeleteMemory operation.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

DeleteMemoryRequest

Request message for MemoryBankService.DeleteMemory.

Fields
name	string Required. The resource name of the Memory to delete. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}/memories/{memory}

DeleteMetadataStoreOperationMetadata

Details of operations that perform MetadataService.DeleteMetadataStore.

Fields
generic_metadata	GenericOperationMetadata Operation metadata for deleting a MetadataStore.

DeleteMetadataStoreRequest

Request message for MetadataService.DeleteMetadataStore.

Fields
name	string Required. The resource name of the MetadataStore to delete. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}
force (deprecated)	bool This item is deprecated! Deprecated: Field is no longer supported.

DeleteModelDeploymentMonitoringJobRequest

Request message for JobService.DeleteModelDeploymentMonitoringJob.

Fields
name	string Required. The resource name of the model monitoring job to delete. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}

DeleteModelMonitorRequest

Request message for ModelMonitoringService.DeleteModelMonitor.

Fields
name	string Required. The name of the ModelMonitor resource to be deleted. Format: projects/{project}/locations/{location}/modelMonitords/{model_monitor}
force	bool Optional. Force delete the model monitor with schedules.

DeleteModelMonitoringJobRequest

Request message for ModelMonitoringService.DeleteModelMonitoringJob.

Fields
name	string Required. The resource name of the model monitoring job to delete. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}/modelMonitoringJobs/{model_monitoring_job}

DeleteModelRequest

Request message for ModelService.DeleteModel.

Fields
name	string Required. The name of the Model resource to be deleted. Format: projects/{project}/locations/{location}/models/{model}

DeleteModelVersionRequest

Request message for ModelService.DeleteModelVersion.

Fields
name	string Required. The name of the model version to be deleted, with a version ID explicitly included. Example: projects/{project}/locations/{location}/models/{model}@1234

DeleteNotebookExecutionJobRequest

Request message for [NotebookService.DeleteNotebookExecutionJob]

Fields
name	string Required. The name of the NotebookExecutionJob resource to be deleted.

DeleteNotebookRuntimeRequest

Request message for NotebookService.DeleteNotebookRuntime.

Fields
name	string Required. The name of the NotebookRuntime resource to be deleted. Instead of checking whether the name is in valid NotebookRuntime resource name format, directly throw NotFound exception if there is no such NotebookRuntime in spanner.

DeleteNotebookRuntimeTemplateRequest

Request message for NotebookService.DeleteNotebookRuntimeTemplate.

Fields
name	string Required. The name of the NotebookRuntimeTemplate resource to be deleted. Format: projects/{project}/locations/{location}/notebookRuntimeTemplates/{notebook_runtime_template}

DeleteOnlineEvaluatorOperationMetadata

Metadata for the DeleteOnlineEvaluator operation.

Fields
generic_metadata	GenericOperationMetadata Generic operation metadata.

DeleteOnlineEvaluatorRequest

Request message for DeleteOnlineEvaluator.

Fields
name	string Required. The name of the OnlineEvaluator to delete. Format: projects/{project}/locations/{location}/onlineEvaluators/{id}.

DeleteOperationMetadata

Details of operations that perform deletes of any entities.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

DeletePersistentResourceRequest

Request message for PersistentResourceService.DeletePersistentResource.

Fields
name	string Required. The name of the PersistentResource to be deleted. Format: projects/{project}/locations/{location}/persistentResources/{persistent_resource}

DeletePipelineJobRequest

Request message for PipelineService.DeletePipelineJob.

Fields
name	string Required. The name of the PipelineJob resource to be deleted. Format: projects/{project}/locations/{location}/pipelineJobs/{pipeline_job}

DeleteRagCorpusRequest

Request message for VertexRagDataService.DeleteRagCorpus.

Fields
name	string Required. The name of the RagCorpus resource to be deleted. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}
force	bool Optional. If set to true, any RagFiles in this RagCorpus will also be deleted. Otherwise, the request will only work if the RagCorpus has no RagFiles.
force_delete	bool Optional. If set to true, any errors generated by external vector database during the deletion will be ignored. The default value is false.

DeleteRagDataSchemaRequest

Request message for VertexRagDataService.DeleteRagDataSchema.

Fields
name	string Required. The name of the RagDataSchema resource to be deleted. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}/ragDataSchemas/{rag_data_schema}

DeleteRagFileRequest

Request message for VertexRagDataService.DeleteRagFile.

Fields
name	string Required. The name of the RagFile resource to be deleted. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}/ragFiles/{rag_file}
force_delete	bool Optional. If set to true, any errors generated by external vector database during the deletion will be ignored. The default value is false.

DeleteRagMetadataRequest

Request message for VertexRagDataService.DeleteRagMetadata.

Fields
name	string Required. The name of the RagMetadata resource to be deleted. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}/ragFiles/{rag_file}/ragMetadata/{rag_metadata}

DeleteReasoningEngineRequest

Request message for ReasoningEngineService.DeleteReasoningEngine.

Fields
name	string Required. The name of the ReasoningEngine resource to be deleted. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}
force	bool Optional. If set to true, child resources of this reasoning engine will also be deleted. Otherwise, the request will fail with FAILED_PRECONDITION error when the reasoning engine has undeleted child resources.

DeleteReasoningEngineRuntimeRevisionOperationMetadata

Metadata associated with DeleteReasoningEngineRuntimeRevision operation.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

DeleteReasoningEngineRuntimeRevisionRequest

Request message for ReasoningEngineRuntimeRevisionService.DeleteReasoningEngineRuntimeRevision.

Fields
name	string Required. The name of the ReasoningEngineRuntimeRevision resource to be deleted. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}/runtimeRevisions/{runtime_revision}

DeleteResponseRequest

Request message for PredictionService.DeleteResponse.

Fields
name	string Required. The name of the Response resource to be deleted. Format: projects/{project}/locations/{location}/endpoints/{endpoint}/responses/{response}

DeleteSavedQueryRequest

Request message for DatasetService.DeleteSavedQuery.

Fields
name	string Required. The resource name of the SavedQuery to delete. Format: projects/{project}/locations/{location}/datasets/{dataset}/savedQueries/{saved_query}

DeleteScheduleRequest

Request message for ScheduleService.DeleteSchedule.

Fields
name	string Required. The name of the Schedule resource to be deleted. Format: projects/{project}/locations/{location}/schedules/{schedule}

DeleteSessionRequest

Request message for SessionService.DeleteSession.

Fields
name	string Required. The resource name of the session. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}/sessions/{session}

DeleteSkillOperationMetadata

Details of SkillRegistryService.DeleteSkill operation.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

DeleteSkillRequest

Request message for SkillRegistryService.DeleteSkill.

Fields
name	string Required. The resource name of the Skill to delete. Format: projects/{project}/locations/{location}/skills/{skill}

DeleteSpecialistPoolRequest

Request message for SpecialistPoolService.DeleteSpecialistPool.

Fields
name	string Required. The resource name of the SpecialistPool to delete. Format: projects/{project}/locations/{location}/specialistPools/{specialist_pool}
force	bool If set to true, any specialist managers in this SpecialistPool will also be deleted. (Otherwise, the request will only work if the SpecialistPool has no specialist managers.)

DeleteStudyRequest

Request message for VizierService.DeleteStudy.

Fields
name	string Required. The name of the Study resource to be deleted. Format: projects/{project}/locations/{location}/studies/{study}

DeleteTensorboardExperimentRequest

Request message for TensorboardService.DeleteTensorboardExperiment.

Fields
name	string Required. The name of the TensorboardExperiment to be deleted. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}

DeleteTensorboardRequest

Request message for TensorboardService.DeleteTensorboard.

Fields
name	string Required. The name of the Tensorboard to be deleted. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

DeleteTensorboardRunRequest

Request message for TensorboardService.DeleteTensorboardRun.

Fields
name	string Required. The name of the TensorboardRun to be deleted. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}

DeleteTensorboardTimeSeriesRequest

Request message for TensorboardService.DeleteTensorboardTimeSeries.

Fields
name	string Required. The name of the TensorboardTimeSeries to be deleted. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}

DeleteTrainingPipelineRequest

Request message for PipelineService.DeleteTrainingPipeline.

Fields
name	string Required. The name of the TrainingPipeline resource to be deleted. Format: projects/{project}/locations/{location}/trainingPipelines/{training_pipeline}

DeleteTrialRequest

Request message for VizierService.DeleteTrial.

Fields
name	string Required. The Trial's name. Format: projects/{project}/locations/{location}/studies/{study}/trials/{trial}

DeployIndexOperationMetadata

Runtime operation information for IndexEndpointService.DeployIndex.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.
deployed_index_id	string The unique index id specified by user

DeployIndexRequest

Request message for IndexEndpointService.DeployIndex.

Fields
index_endpoint	string Required. The name of the IndexEndpoint resource into which to deploy an Index. Format: projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}
deployed_index	DeployedIndex Required. The DeployedIndex to be created within the IndexEndpoint.

DeployIndexResponse

Response message for IndexEndpointService.DeployIndex.

Fields
deployed_index	DeployedIndex The DeployedIndex that had been deployed in the IndexEndpoint.

DeployModelOperationMetadata

Runtime operation information for EndpointService.DeployModel.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.
deployment_stage	DeploymentStage Output only. The deployment stage of the model.

DeployModelRequest

Request message for EndpointService.DeployModel.

Fields
endpoint	string Required. The name of the Endpoint resource into which to deploy a Model. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
deployed_model	DeployedModel Required. The DeployedModel to be created within the Endpoint. Note that Endpoint.traffic_split must be updated for the DeployedModel to start receiving traffic, either as part of this call, or via EndpointService.UpdateEndpoint.
traffic_split	map<string, int32> A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If this field is non-empty, then the Endpoint's traffic_split will be overwritten with it. To refer to the ID of the just being deployed Model, a "0" should be used, and the actual ID of the new DeployedModel will be filled in its place by this method. The traffic percentage values must add up to 100. If this field is empty, then the Endpoint's traffic_split is not updated.

DeployModelResponse

Response message for EndpointService.DeployModel.

Fields
deployed_model	DeployedModel The DeployedModel that had been deployed in the Endpoint.

DeployOperationMetadata

Runtime operation information for ModelGardenService.Deploy.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.
publisher_model	string Output only. The name of the model resource.
destination	string Output only. The resource name of the Location to deploy the model in. Format: projects/{project}/locations/{location}
project_number	int64 Output only. The project number where the deploy model request is sent.
model_id	string Output only. The model id to be used at query time.

DeployPublisherModelOperationMetadata

This item is deprecated!

Runtime operation information for ModelGardenService.DeployPublisherModel.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.
publisher_model	string Output only. The name of the PublisherModel resource. Format: publishers/{publisher}/models/{publisher_model}@{version_id}, or publishers/hf-{hugging-face-author}/models/{hugging-face-model-name}@001
destination	string Output only. The resource name of the Location to deploy the model in. Format: projects/{project}/locations/{location}
project_number	int64 Output only. The project number where the deploy model request is sent.

DeployPublisherModelRequest

This item is deprecated!

Request message for ModelGardenService.DeployPublisherModel.

Fields
model	string Required. The model to deploy. Format: 1. publishers/{publisher}/models/{publisher_model}@{version_id}, or publishers/hf-{hugging-face-author}/models/{hugging-face-model-name}@001. 2. Hugging Face model ID like google/gemma-2-2b-it. 3. Custom model Google Cloud Storage URI like gs://bucket. 4. Custom model zip file like https://example.com/a.zip.
destination	string Required. The resource name of the Location to deploy the model in. Format: projects/{project}/locations/{location}
endpoint_display_name	string Optional. The user-specified display name of the endpoint. If not set, a default name will be used.
dedicated_resources	DedicatedResources Optional. The dedicated resources to use for the endpoint. If not set, the default resources will be used.
model_display_name	string Optional. The user-specified display name of the uploaded model. If not set, a default name will be used.
hugging_face_access_token	string Optional. The Hugging Face read access token used to access the model artifacts of gated models.
accept_eula	bool Optional. Whether the user accepts the End User License Agreement (EULA) for the model.

DeployPublisherModelResponse

This item is deprecated!

Response message for ModelGardenService.DeployPublisherModel.

Fields
publisher_model	string Output only. The name of the PublisherModel resource. Format: publishers/{publisher}/models/{publisher_model}@{version_id}, or publishers/hf-{hugging-face-author}/models/{hugging-face-model-name}@001
endpoint	string Output only. The name of the Endpoint created. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
model	string Output only. The name of the Model created. Format: projects/{project}/locations/{location}/models/{model}

DeployRequest

Request message for ModelGardenService.Deploy.

Fields
destination	string Required. The resource name of the Location to deploy the model in. Format: projects/{project}/locations/{location}
model_config	ModelConfig Optional. The model config to use for the deployment. If not specified, the default model config will be used.
endpoint_config	EndpointConfig Optional. The endpoint config to use for the deployment. If not specified, the default endpoint config will be used.
deploy_config	DeployConfig Optional. The deploy config to use for the deployment. If not specified, the default deploy config will be used.
Union field artifacts. The artifacts to deploy. artifacts can be only one of the following:
publisher_model_name	string The Model Garden model to deploy. Format: publishers/{publisher}/models/{publisher_model}@{version_id}, or publishers/hf-{hugging-face-author}/models/{hugging-face-model-name}@001.
hugging_face_model_id	string The Hugging Face model to deploy. Format: Hugging Face model ID like google/gemma-2-2b-it.
custom_model	CustomModel The custom model to deploy from a Google Cloud Storage URI.

CustomModel

The custom model to deploy from model weights in a Google Cloud Storage URI or Model Registry model.

Fields
model_id (deprecated)	string This item is deprecated! Optional. Deprecated. Use ModelConfig.model_user_id instead.
Union field model_source. The source of the custom model. model_source can be only one of the following:
gcs_uri	string Immutable. The Google Cloud Storage URI of the custom model, storing weights and config files (which can be used to infer the base model).

DeployConfig

The deploy config to use for the deployment.

Fields
dedicated_resources	DedicatedResources Optional. The dedicated resources to use for the endpoint. If not set, the default resources will be used.
fast_tryout_enabled	bool Optional. If true, enable the QMT fast tryout feature for this model if possible.
system_labels	map<string, string> Optional. System labels for Model Garden deployments. These labels are managed by Google and for tracking purposes only.

EndpointConfig

The endpoint config to use for the deployment.

Fields
endpoint_display_name	string Optional. The user-specified display name of the endpoint. If not set, a default name will be used.
dedicated_endpoint_enabled (deprecated)	bool This item is deprecated! Optional. Deprecated. Use dedicated_endpoint_disabled instead. If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitations will be removed soon.
dedicated_endpoint_disabled	bool Optional. By default, if dedicated endpoint is enabled and private service connect config is not set, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. If private service connect config is set, the endpoint will be exposed through private service connect. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitations will be removed soon. If this field is set to true, the dedicated endpoint will be disabled and the deployed model will be exposed through the shared DNS {region}-aiplatform.googleapis.com.
private_service_connect_config	PrivateServiceConnectConfig Optional. Configuration for private service connect. If set, the endpoint will be exposed through private service connect.
labels	map<string, string> Optional. The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
endpoint_user_id	string Optional. Immutable. The ID to use for endpoint, which will become the final component of the endpoint resource name. If not provided, Agent Platform will generate a value for this ID. If the first character is a letter, this value may be up to 63 characters, and valid characters are [a-z0-9-]. The last character must be a letter or number. If the first character is a number, this value may be up to 9 characters, and valid characters are [0-9] with no leading zeros. When using HTTP/JSON, this field is populated based on a query string argument, such as ?endpoint_id=12345. This is the fallback for fields that are not included in either the URI or the body.

ModelConfig

The model config to use for the deployment.

Fields
accept_eula	bool Optional. Whether the user accepts the End User License Agreement (EULA) for the model.
hugging_face_access_token	string Optional. The Hugging Face read access token used to access the model artifacts of gated models.
hugging_face_cache_enabled	bool Optional. If true, the model will deploy with a cached version instead of directly downloading the model artifacts from Hugging Face. This is suitable for VPC-SC users with limited internet access.
model_display_name	string Optional. The user-specified display name of the uploaded model. If not set, a default name will be used.
container_spec	ModelContainerSpec Optional. The specification of the container that is to be used when deploying. If not set, the default container spec will be used.
model_user_id	string Optional. The ID to use for the uploaded Model, which will become the final component of the model resource name. When not provided, Agent Platform will generate a value for this ID. When Model Registry model is provided, this field will be ignored. This value may be up to 63 characters, and valid characters are [a-z0-9_-]. The first character cannot be a number or hyphen.

DeployResponse

Response message for ModelGardenService.Deploy.

Fields
publisher_model	string Output only. The name of the PublisherModel resource. Format: publishers/{publisher}/models/{publisher_model}@{version_id}, or publishers/hf-{hugging-face-author}/models/{hugging-face-model-name}@001
endpoint	string Output only. The name of the Endpoint created. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
model	string Output only. The name of the Model created. Format: projects/{project}/locations/{location}/models/{model}

DeploySolverOperationMetadata

Runtime operation information for SolverService.DeploySolver.

Fields
generic_metadata	GenericOperationMetadata The generic operation information.

DeployedIndex

A deployment of an Index. IndexEndpoints contain one or more DeployedIndexes.

Fields
id	string Required. The user specified ID of the DeployedIndex. The ID can be up to 128 characters long and must start with a letter and only contain letters, numbers, and underscores. The ID must be unique within the project it is created in.
index	string Required. The name of the Index this is the deployment of. We may refer to this Index as the DeployedIndex's "original" Index.
display_name	string The display name of the DeployedIndex. If not provided upon creation, the Index's display_name is used.
create_time	Timestamp Output only. Timestamp when the DeployedIndex was created.
private_endpoints	IndexPrivateEndpoints Output only. Provides paths for users to send requests directly to the deployed index services running on Cloud via private services access. This field is populated if network is configured.
index_sync_time	Timestamp Output only. The DeployedIndex may depend on various data on its original Index. Additionally when certain changes to the original Index are being done (e.g. when what the Index contains is being changed) the DeployedIndex may be asynchronously updated in the background to reflect these changes. If this timestamp's value is at least the Index.update_time of the original Index, it means that this DeployedIndex and the original Index are in sync. If this timestamp is older, then to see which updates this DeployedIndex already contains (and which it does not), one must list the operations that are running on the original Index. Only the successfully completed Operations with update_time equal or before this sync time are contained in this DeployedIndex.
automatic_resources	AutomaticResources Optional. A description of resources that the DeployedIndex uses, which to large degree are decided by Agent Platform, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2 (we don't provide SLA when min_replica_count=1). If max_replica_count is not set, the default value is min_replica_count. The max allowed replica count is 1000.
dedicated_resources	DedicatedResources Optional. A description of resources that are dedicated to the DeployedIndex, and that need a higher degree of manual configuration. The field min_replica_count must be set to a value strictly greater than 0, or else validation will fail. We don't provide SLA when min_replica_count=1. If max_replica_count is not set, the default value is min_replica_count. The max allowed replica count is 1000. Available machine types for SMALL shard: e2-standard-2 and all machine types available for MEDIUM and LARGE shard. Available machine types for MEDIUM shard: e2-standard-16 and all machine types available for LARGE shard. Available machine types for LARGE shard: e2-highmem-16, n2d-standard-32. n1-standard-16 and n1-standard-32 are still available, but we recommend e2-standard-16 and e2-highmem-16 for cost efficiency.
enable_access_logging	bool Optional. If true, private endpoint's access logs are sent to Cloud Logging. These logs are like standard server access logs, containing information like timestamp and latency for each MatchRequest. Note that logs may incur a cost, especially if the deployed index receives a high queries per second rate (QPS). Estimate your costs before enabling this option.
enable_datapoint_upsert_logging	bool Optional. If true, logs to Cloud Logging errors relating to datapoint upserts. Under normal operation conditions, these log entries should be very rare. However, if incompatible datapoint updates are being uploaded to an index, a high volume of log entries may be generated in a short period of time. Note that logs may incur a cost, especially if the deployed index receives a high volume of datapoint upserts. Estimate your costs before enabling this option.
deployed_index_auth_config	DeployedIndexAuthConfig Optional. If set, the authentication is enabled for the private endpoint.
reserved_ip_ranges[]	string Optional. A list of reserved ip ranges under the VPC network that can be used for this DeployedIndex. If set, we will deploy the index within the provided ip ranges. Otherwise, the index might be deployed to any ip ranges under the provided VPC network. The value should be the name of the address (https://cloud.google.com/compute/docs/reference/rest/v1/addresses) Example: ['vertex-ai-ip-range']. For more information about subnets and network IP ranges, please see https://cloud.google.com/vpc/docs/subnets#manually_created_subnet_ip_ranges.
deployment_group	string Optional. The deployment group can be no longer than 64 characters (eg: 'test', 'prod'). If not set, we will use the 'default' deployment group. Creating deployment_groups with reserved_ip_ranges is a recommended practice when the peered network has multiple peering ranges. This creates your deployments from predictable IP spaces for easier traffic administration. Also, one deployment_group (except 'default') can only be used with the same reserved_ip_ranges which means if the deployment_group has been used with reserved_ip_ranges: [a, b, c], using it with [a, b] or [d, e] is disallowed. Note: we only support up to 5 deployment groups(not including 'default').
deployment_tier	DeploymentTier Optional. The deployment tier that the index is deployed to. DEPLOYMENT_TIER_UNSPECIFIED will use a system-chosen default tier.
psc_automation_configs[]	PSCAutomationConfig Optional. If set for PSC deployed index, PSC connection will be automatically created after deployment is done and the endpoint information is populated in private_endpoints.psc_automated_endpoints.

DeploymentTier

Tiers encapsulate serving time attributes like latency and throughput.

Enums
DEPLOYMENT_TIER_UNSPECIFIED	Default deployment tier.
STORAGE	Optimized for costs.

DeployedIndexAuthConfig

Used to set up the auth on the DeployedIndex's private endpoint.

Fields
auth_provider	AuthProvider Defines the authentication provider that the DeployedIndex uses.

AuthProvider

Configuration for an authentication provider, including support for JSON Web Token (JWT).

Fields
audiences[]	string The list of JWT audiences. that are allowed to access. A JWT containing any of these audiences will be accepted.
allowed_issuers[]	string A list of allowed JWT issuers. Each entry must be a valid Google service account, in the following format: [e-mail]

DeployedIndexRef

Points to a DeployedIndex.

Fields
index_endpoint	string Immutable. A resource name of the IndexEndpoint.
deployed_index_id	string Immutable. The ID of the DeployedIndex in the above IndexEndpoint.
display_name	string Output only. The display name of the DeployedIndex.

DeployedModel

A deployment of a Model. Endpoints contain one or more DeployedModels.

Fields
id	string Immutable. The ID of the DeployedModel. If not provided upon deployment, Agent Platform will generate a value for this ID. This value should be 1-10 characters, and valid characters are /[0-9]/.
model	string The resource name of the Model that this is the deployment of. Note that the Model may be in a different location than the DeployedModel's Endpoint. The resource name may contain version id or version alias to specify the version. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden if no version is specified, the default version will be deployed.
gdc_connected_model	string GDC pretrained / Gemini model name. The model name is a plain model name, e.g. gemini-1.5-flash-002.
model_version_id	string Output only. The version ID of the model that is deployed.
display_name	string The display name of the DeployedModel. If not provided upon creation, the Model's display_name is used.
create_time	Timestamp Output only. Timestamp when the DeployedModel was created.
explanation_spec	ExplanationSpec Explanation configuration for this DeployedModel. When deploying a Model using EndpointService.DeployModel, this value overrides the value of Model.explanation_spec. All fields of explanation_spec are optional in the request. If a field of explanation_spec is not populated, the value of the same field of Model.explanation_spec is inherited. If the corresponding Model.explanation_spec is not populated, all fields of the explanation_spec will be used for the explanation configuration.
disable_explanations	bool If true, deploy the model without explainable feature, regardless the existence of Model.explanation_spec or explanation_spec.
service_account	string The service account that the DeployedModel's container runs as. Specify the email address of the service account. If this service account is not specified, the container runs as a service account that doesn't have access to the resource project. Users deploying the Model must have the iam.serviceAccounts.actAs permission on this service account.
enable_container_logging	bool If true, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging. Only supported for custom-trained Models and AutoML Tabular Models.
disable_container_logging	bool For custom-trained Models and AutoML Tabular Models, the container of the DeployedModel instances will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
enable_access_logging	bool If true, online prediction access logs are sent to Cloud Logging. These logs are like standard server access logs, containing information like timestamp and latency for each prediction request. Note that logs may incur a cost, especially if your project receives prediction requests at a high queries per second rate (QPS). Estimate your costs before enabling this option.
private_endpoints	PrivateEndpoints Output only. Provide paths for users to send predict/explain/health requests directly to the deployed model services running on Cloud via private services access. This field is populated if network is configured.
faster_deployment_config	FasterDeploymentConfig Configuration for faster model deployment.
rollout_options	RolloutOptions Options for configuring rolling deployments.
status	Status Output only. Runtime status of the deployed model.
system_labels	map<string, string> System labels to apply to Model Garden deployments. System labels are managed by Google for internal use only.
checkpoint_id	string The checkpoint id of the model.
speculative_decoding_spec	SpeculativeDecodingSpec Optional. Spec for configuring speculative decoding.
Union field prediction_resources. The prediction (for example, the machine) resources that the DeployedModel uses. The user is billed for the resources (at least their minimal amount) even if the DeployedModel receives no traffic. Not all Models support all resources types. See Model.supported_deployment_resources_types. Required except for Large Model Deploy use cases. prediction_resources can be only one of the following:
dedicated_resources	DedicatedResources A description of resources that are dedicated to the DeployedModel, and that need a higher degree of manual configuration.
automatic_resources	AutomaticResources A description of resources that to large degree are decided by Agent Platform, and require only a modest additional configuration.
shared_resources	string The resource name of the shared DeploymentResourcePool to deploy on. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}
full_fine_tuned_resources	FullFineTunedResources Optional. Resources for a full fine tuned model.

Status

Runtime status of the deployed model.

Fields
message	string Output only. The latest deployed model's status message (if any).
last_update_time	Timestamp Output only. The time at which the status was last updated.
available_replica_count	int32 Output only. The number of available replicas of the deployed model.

DeployedModelRef

Points to a DeployedModel.

Fields
endpoint	string Immutable. A resource name of an Endpoint.
deployed_model_id	string Immutable. An ID of a DeployedModel in the above Endpoint.
checkpoint_id	string Immutable. The ID of the Checkpoint deployed in the DeployedModel.

DeploymentResourcePool

A description of resources that can be shared by multiple DeployedModels, whose underlying specification consists of a DedicatedResources.

Fields
name	string Immutable. The resource name of the DeploymentResourcePool. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}
dedicated_resources	DedicatedResources Required. The underlying DedicatedResources that the DeploymentResourcePool uses.
encryption_spec	EncryptionSpec Customer-managed encryption key spec for a DeploymentResourcePool. If set, this DeploymentResourcePool will be secured by this key. Endpoints and the DeploymentResourcePool they deploy in need to have the same EncryptionSpec.
service_account	string The service account that the DeploymentResourcePool's container(s) run as. Specify the email address of the service account. If this service account is not specified, the container(s) run as a service account that doesn't have access to the resource project. Users deploying the Models to this DeploymentResourcePool must have the iam.serviceAccounts.actAs permission on this service account.
disable_container_logging	bool If the DeploymentResourcePool is deployed with custom-trained Models or AutoML Tabular Models, the container(s) of the DeploymentResourcePool will send stderr and stdout streams to Cloud Logging by default. Please note that the logs incur cost, which are subject to Cloud Logging pricing. User can disable container logging by setting this flag to true.
create_time	Timestamp Output only. Timestamp when this DeploymentResourcePool was created.
satisfies_pzs	bool Output only. Reserved for future use.
satisfies_pzi	bool Output only. Reserved for future use.

DeploymentStage

Stage field indicating the current progress of a deployment.

Enums
DEPLOYMENT_STAGE_UNSPECIFIED	Default value. This value is unused.
STARTING_DEPLOYMENT	The deployment is initializing and setting up the environment.
PREPARING_MODEL	The deployment is preparing the model assets.
CREATING_SERVING_CLUSTER	The deployment is creating the underlying serving cluster.
ADDING_NODES_TO_CLUSTER	The deployment is adding nodes to the serving cluster.
GETTING_CONTAINER_IMAGE	The deployment is getting the container image for the model server.
STARTING_MODEL_SERVER	The deployment is starting the model server.
FINISHING_UP	The deployment is performing finalization steps.
DEPLOYMENT_TERMINATED	The deployment has terminated.
SUCCESSFULLY_DEPLOYED	The deployment has succeeded.
FAILED_TO_DEPLOY	The deployment has failed.

DestinationFeatureSetting

Fields
feature_id	string Required. The ID of the Feature to apply the setting to.
destination_field	string Specify the field name in the export destination. If not specified, Feature ID is used.

DirectPredictRequest

Request message for PredictionService.DirectPredict.

Fields
endpoint	string Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
inputs[]	Tensor The prediction input.
parameters	Tensor The parameters that govern the prediction.

DirectPredictResponse

Response message for PredictionService.DirectPredict.

Fields
outputs[]	Tensor The prediction output.
parameters	Tensor The parameters that govern the prediction.

DirectRawPredictRequest

Request message for PredictionService.DirectRawPredict.

Fields
endpoint	string Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
method_name	string Fully qualified name of the API method being invoked to perform predictions. Format: /namespace.Service/Method/ Example: /tensorflow.serving.PredictionService/Predict
input	bytes The prediction input.

DirectRawPredictResponse

Response message for PredictionService.DirectRawPredict.

Fields
output	bytes The prediction output.

DirectUploadSource

This type has no fields.

The input content is encapsulated and uploaded in the request.

DiskSpec

Represents the spec of disk options.

Fields
boot_disk_type	string Type of the boot disk. For non-A3U machines, the default value is "pd-ssd", for A3U machines, the default value is "hyperdisk-balanced". Valid values: "pd-ssd" (Persistent Disk Solid State Drive), "pd-standard" (Persistent Disk Hard Disk Drive) or "hyperdisk-balanced".
boot_disk_size_gb	int32 Size in GB of the boot disk (default is 100GB).

DistillationDataStats

Statistics for distillation prompt dataset. These statistics do not include the responses sampled from the teacher model.

Fields
training_dataset_stats	DatasetStats Output only. Statistics computed for the training dataset.

DistillationHyperParameters

Hyperparameters for Distillation.

Fields
adapter_size	AdapterSize Optional. Adapter size for distillation.
learning_rate	double Optional. Specifies the learning rate for tuning. Mutually exclusive with learning_rate_multiplier. This feature is only available for open source models.
batch_size	int64 Optional. Batch size for tuning. This feature is only available for open source models.
epoch_count	int64 Optional. Number of complete passes the model makes over the entire training dataset during training.
learning_rate_multiplier	double Optional. Multiplier for adjusting the default learning rate.

DistillationSpec

Tuning Spec for Distillation.

Fields
training_dataset_uri (deprecated)	string This item is deprecated! Deprecated. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.
prompt_dataset_uri	string Optional. Cloud Storage path to file containing prompt dataset for distillation. The dataset must be formatted as a JSONL file.
hyper_parameters	DistillationHyperParameters Optional. Hyperparameters for Distillation.
student_model (deprecated)	string This item is deprecated! The student model that is being tuned, e.g., "google/gemma-2b-1.1-it". Deprecated. Use base_model instead.
pipeline_root_directory (deprecated)	string This item is deprecated! Deprecated. A path in a Cloud Storage bucket, which will be treated as the root output directory of the distillation pipeline. It is used by the system to generate the paths of output artifacts.
tuning_mode	TuningMode Optional. Specifies the tuning mode for distillation (sft part). This feature is only available for open source models.
Union field teacher_model. The teacher model that is being distilled from. See Supported models. teacher_model can be only one of the following:
base_teacher_model	string The base teacher model that is being distilled. See Supported models.
tuned_teacher_model_source	string The resource name of the Tuned teacher model. Format: projects/{project}/locations/{location}/models/{model}.
validation_dataset_uri	string Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.

DnsPeeringConfig

DNS peering configuration. These configurations are used to create DNS peering zones in the Vertex tenant project VPC, enabling resolution of records within the specified domain hosted in the target network's Cloud DNS.

Fields
domain	string Required. The DNS name suffix of the zone being peered to, e.g., "my-internal-domain.corp.". Must end with a dot.
target_project	string Required. The project ID hosting the Cloud DNS managed zone that contains the 'domain'. The Agent Platform Service Agent requires the dns.peer role on this project.
target_network	string Required. The VPC network name in the target_project where the DNS zone specified by 'domain' is visible.

DoubleArray

A list of double values.

Fields
values[]	double A list of double values.

DynamicRetrievalConfig

Describes the options to customize dynamic retrieval.

Fields
mode	Mode The mode of the predictor to be used in dynamic retrieval.
dynamic_threshold	float Optional. The threshold to be used in dynamic retrieval. If not set, a system default value is used.

Mode

The mode of the predictor to be used in dynamic retrieval.

Enums
MODE_UNSPECIFIED	Always trigger retrieval.
MODE_DYNAMIC	Run retrieval only when system decides it is necessary.

EmbedContentRequest

Request message for PredictionService.EmbedContent.

Fields
model	string Required. The name of the publisher model requested to serve the prediction. Format: projects/{project}/locations/{location}/publishers/*/models/*
content	Content Required. The content to be embedded.
title (deprecated)	string This item is deprecated! Optional. Deprecated: Please use EmbedContentConfig.title instead. The title for the text.
task_type (deprecated)	EmbeddingTaskType This item is deprecated! Optional. Deprecated: Please use EmbedContentConfig.task_type instead. The task type of the embedding.
output_dimensionality (deprecated)	int32 This item is deprecated! Optional. Deprecated: Please use EmbedContentConfig.output_dimensionality instead. Reduced dimension for the output embedding. If set, excessive values in the output embedding are truncated from the end.
auto_truncate (deprecated)	bool This item is deprecated! Optional. Deprecated: Please use EmbedContentConfig.auto_truncate instead. Whether to silently truncate the input content if it's longer than the maximum sequence length.
embed_content_config	EmbedContentConfig Optional. Configuration for the EmbedContent request.

EmbedContentConfig

Configurations for the EmbedContent API.

Fields
title	string Optional. The title for the text. Only applicable to text-only embedding models.
task_type	EmbeddingTaskType Optional. The task type of the embedding. Only applicable to text-only embedding models.
auto_truncate	bool Optional. Whether to silently truncate the input content if it's longer than the maximum sequence length. Only applicable to text-only embedding models.
output_dimensionality	int32 Optional. Reduced dimension for the output embedding. If set, excessive values in the output embedding are truncated from the end.
document_ocr	bool Optional. Whether to enable OCR for document content.
audio_track_extraction	bool Optional. Whether to extract audio from video content.

EmbeddingTaskType

Represents a downstream task the embeddings will be used for.

Enums
UNSPECIFIED	Unset value, which will default to one of the other enum values.
RETRIEVAL_QUERY	Specifies the given text is a query in a search/retrieval setting.
RETRIEVAL_DOCUMENT	Specifies the given text is a document from the corpus being searched.
SEMANTIC_SIMILARITY	Specifies the given text will be used for STS.
CLASSIFICATION	Specifies that the given text will be classified.
CLUSTERING	Specifies that the embeddings will be used for clustering.
QUESTION_ANSWERING	Specifies that the embeddings will be used for question answering.
FACT_VERIFICATION	Specifies that the embeddings will be used for fact verification.
CODE_RETRIEVAL_QUERY	Specifies that the embeddings will be used for code retrieval.

EmbedContentResponse

Response message for PredictionService.EmbedContent.

Fields
embedding	Embedding The embedding generated from the input content.
usage_metadata	UsageMetadata Usage metadata about the response(s).
truncated	bool Whether the input content was truncated before generating the embedding.

Embedding

A list of floats representing an embedding.

Fields
values[]	float Embedding vector values.

EncryptionSpec

Represents a customer-managed encryption key specification that can be applied to a Agent Platform resource.

Fields
kms_key_name	string Required. Resource name of the Cloud KMS key used to protect the resource. The Cloud KMS key must be in the same region as the resource. It must have the format projects/{project}/locations/{location}/keyRings/{key_ring}/cryptoKeys/{crypto_key}.

Endpoint

Models are deployed into it, and afterwards Endpoint is called to obtain predictions and explanations.

Fields
name	string Identifier. The resource name of the Endpoint.
display_name	string Required. The display name of the Endpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.
description	string The description of the Endpoint.
deployed_models[]	DeployedModel Output only. The models deployed in this Endpoint. To add or remove DeployedModels use EndpointService.DeployModel and EndpointService.UndeployModel respectively.
traffic_split	map<string, int32> A map from a DeployedModel's ID to the percentage of this Endpoint's traffic that should be forwarded to that DeployedModel. If a DeployedModel's ID is not listed in this map, then it receives no traffic. The traffic percentage values must add up to 100, or map must be empty if the Endpoint is to not accept any traffic at a moment.
etag	string Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map<string, string> The labels with user-defined metadata to organize your Endpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
create_time	Timestamp Output only. Timestamp when this Endpoint was created.
update_time	Timestamp Output only. Timestamp when this Endpoint was last updated.
encryption_spec	EncryptionSpec Customer-managed encryption key spec for an Endpoint. If set, this Endpoint and all sub-resources of this Endpoint will be secured by this key.
network	string Optional. The full name of the Google Compute Engine network to which the Endpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. Only one of the fields, network or enable_private_service_connect, can be set. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is network name.
enable_private_service_connect (deprecated)	bool This item is deprecated! Deprecated: If true, expose the Endpoint via private service connect. Only one of the fields, network or enable_private_service_connect, can be set.
private_service_connect_config	PrivateServiceConnectConfig Optional. Configuration for private service connect. network and private_service_connect_config are mutually exclusive.
model_deployment_monitoring_job	string Output only. Resource name of the Model Monitoring job associated with this Endpoint if monitoring is enabled by JobService.CreateModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}
predict_request_response_logging_config	PredictRequestResponseLoggingConfig Configures the request-response logging for online prediction.
dedicated_endpoint_enabled	bool If true, the endpoint will be exposed through a dedicated DNS [Endpoint.dedicated_endpoint_dns]. Your request to the dedicated DNS will be isolated from other users' traffic and will have better performance and reliability. Note: Once you enabled dedicated endpoint, you won't be able to send request to the shared DNS {region}-aiplatform.googleapis.com. The limitation will be removed soon.
dedicated_endpoint_dns	string Output only. DNS of the dedicated endpoint. Will only be populated if dedicated_endpoint_enabled is true. Depending on the features enabled, uid might be a random number or a string. For example, if fast_tryout is enabled, uid will be fasttryout. Format: https://{endpoint_id}.{region}-{uid}.prediction.vertexai.goog.
client_connection_config	ClientConnectionConfig Configurations that are applied to the endpoint for online prediction.
satisfies_pzs	bool Output only. Reserved for future use.
satisfies_pzi	bool Output only. Reserved for future use.
gen_ai_advanced_features_config	GenAiAdvancedFeaturesConfig Optional. Configuration for GenAiAdvancedFeatures. If the endpoint is serving GenAI models, advanced features like native RAG integration can be configured. Currently, only Model Garden models are supported.

EnterpriseWebSearch

Tool to search public web data, powered by Agent Platform Search and Sec4 compliance.

Fields
exclude_domains[]	string Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.
blocking_confidence	PhishBlockThreshold Optional. Sites with confidence level chosen & above this value will be blocked from the search results.

EntityIdSelector

Selector for entityId. Getting ids from the given source.

Fields
entity_id_field	string Source column that holds entity IDs. If not provided, entity IDs are extracted from the column named entity_id.
Union field EntityIdsSource. Details about the source data, including the location of the storage and the format. EntityIdsSource can be only one of the following:
csv_source	CsvSource Source of Csv

EntityType

An entity type is a type of object in a system that needs to be modeled and have stored information about. For example, driver is an entity type, and driver0 is an instance of an entity type driver.

Fields
name	string Immutable. Name of the EntityType. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} The last part entity_type is assigned by the client. The entity_type can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z and underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given a featurestore.
description	string Optional. Description of the EntityType.
create_time	Timestamp Output only. Timestamp when this EntityType was created.
update_time	Timestamp Output only. Timestamp when this EntityType was most recently updated.
labels	map<string, string> Optional. The labels with user-defined metadata to organize your EntityTypes. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one EntityType (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
etag	string Optional. Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
monitoring_config	FeaturestoreMonitoringConfig Optional. The default monitoring configuration for all Features with value type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 under this EntityType. If this is populated with [FeaturestoreMonitoringConfig.monitoring_interval] specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring is disabled.
offline_storage_ttl_days	int32 Optional. Config for data retention policy in offline storage. TTL in days for feature values that will be stored in offline storage. The Feature Store offline storage periodically removes obsolete feature values older than offline_storage_ttl_days since the feature generation time. If unset (or explicitly set to 0), default to 4000 days TTL.
satisfies_pzs	bool Output only. Reserved for future use.
satisfies_pzi	bool Output only. Reserved for future use.

EnvVar

Represents an environment variable present in a Container or Python Module.

Fields
name	string Required. Name of the environment variable. Must be a valid C identifier.
value	string Required. Variables that reference a $(VAR_NAME) are expanded using the previous defined environment variables in the container and any service environment variables. If a variable cannot be resolved, the reference in the input string will be unchanged. The $(VAR_NAME) syntax can be escaped with a double $$, ie: $$(VAR_NAME). Escaped references will never be expanded, regardless of whether the variable exists or not.

ErrorAnalysisAnnotation

Model error analysis for each annotation.

Fields
attributed_items[]	AttributedItem Attributed items for a given annotation, typically representing neighbors from the training sets constrained by the query type.
query_type	QueryType The query type used for finding the attributed items.
outlier_score	double The outlier score of this annotated item. Usually defined as the min of all distances from attributed items.
outlier_threshold	double The threshold used to determine if this annotation is an outlier or not.

AttributedItem

Attributed items for a given annotation, typically representing neighbors from the training sets constrained by the query type.

Fields
annotation_resource_name	string The unique ID for each annotation. Used by FE to allocate the annotation in DB.
distance	double The distance of this item to the annotation.

QueryType

The query type used for finding the attributed items.

Enums
QUERY_TYPE_UNSPECIFIED	Unspecified query type for model error analysis.
ALL_SIMILAR	Query similar samples across all classes in the dataset.
SAME_CLASS_SIMILAR	Query similar samples from the same class of the input sample.
SAME_CLASS_DISSIMILAR	Query dissimilar samples from the same class of the input sample.

EvaluateDatasetOperationMetadata

Operation metadata for Dataset Evaluation.

Fields
generic_metadata	GenericOperationMetadata Generic operation metadata.

EvaluateDatasetRequest

Request message for EvaluationService.EvaluateDataset.

Fields
location	string Required. The resource name of the Location to evaluate the dataset. Format: projects/{project}/locations/{location}
dataset	EvaluationDataset Required. The dataset used for evaluation.
metrics[]	Metric Required. The metrics used for evaluation.
output_config	OutputConfig Required. Config for evaluation output.
autorater_config	AutoraterConfig Optional. Autorater config used for evaluation. Currently only publisher Gemini models are supported. Format: projects/{PROJECT}/locations/{LOCATION}/publishers/google/models/{MODEL}.

EvaluateDatasetResponse

The results from an evaluation run performed by the EvaluationService.

Fields
aggregation_output	AggregationOutput Output only. Aggregation statistics derived from results of EvaluationService.
output_info	OutputInfo Output only. Output info for EvaluationService.

EvaluateDatasetRun

Evaluate Dataset Run Result for Tuning Job.

Fields
operation_name (deprecated)	string This item is deprecated! Output only. Deprecated: The updated architecture uses evaluation_run instead.
evaluation_run	string Output only. The resource name of the evaluation run. Format: projects/{project}/locations/{location}/evaluationRuns/{evaluation_run_id}.
checkpoint_id	string Output only. The checkpoint id used in the evaluation run. Only populated when evaluating checkpoints.
evaluate_dataset_response	EvaluateDatasetResponse Output only. Results for EvaluationService.
error	Status Output only. The error of the evaluation run if any.

EvaluateInstancesRequest

Request message for EvaluationService.EvaluateInstances.

Fields
location	string Required. The resource name of the Location to evaluate the instances. Format: projects/{project}/locations/{location}
metrics[]	Metric The metrics used for evaluation. Currently, we only support evaluating a single metric. If multiple metrics are provided, only the first one will be evaluated.
metric_sources[]	MetricSource Optional. The metrics (either inline or registered) used for evaluation. Currently, we only support evaluating a single metric. If multiple metrics are provided, only the first one will be evaluated.
instance	EvaluationInstance The instance to be evaluated.
autorater_config	AutoraterConfig Optional. Autorater config used for evaluation. Not applicable for predefined metrics (PredefinedMetricSpec); the server uses its own model configuration for predefined metrics and this field is ignored.
Union field metric_inputs. Instances and specs for evaluation metric_inputs can be only one of the following:
exact_match_input	ExactMatchInput Auto metric instances. Instances and metric spec for exact match metric.
bleu_input	BleuInput Instances and metric spec for bleu metric.
rouge_input	RougeInput Instances and metric spec for rouge metric.
fluency_input	FluencyInput LLM-based metric instance. General text generation metrics, applicable to other categories. Input for fluency metric.
coherence_input	CoherenceInput Input for coherence metric.
safety_input	SafetyInput Input for safety metric.
groundedness_input	GroundednessInput Input for groundedness metric.
fulfillment_input	FulfillmentInput Input for fulfillment metric.
summarization_quality_input	SummarizationQualityInput Input for summarization quality metric.
pairwise_summarization_quality_input	PairwiseSummarizationQualityInput Input for pairwise summarization quality metric.
summarization_helpfulness_input	SummarizationHelpfulnessInput Input for summarization helpfulness metric.
summarization_verbosity_input	SummarizationVerbosityInput Input for summarization verbosity metric.
question_answering_quality_input	QuestionAnsweringQualityInput Input for question answering quality metric.
pairwise_question_answering_quality_input	PairwiseQuestionAnsweringQualityInput Input for pairwise question answering quality metric.
question_answering_relevance_input	QuestionAnsweringRelevanceInput Input for question answering relevance metric.
question_answering_helpfulness_input	QuestionAnsweringHelpfulnessInput Input for question answering helpfulness metric.
question_answering_correctness_input	QuestionAnsweringCorrectnessInput Input for question answering correctness metric.
pointwise_metric_input	PointwiseMetricInput Input for pointwise metric.
pairwise_metric_input	PairwiseMetricInput Input for pairwise metric.
tool_call_valid_input	ToolCallValidInput Tool call metric instances. Input for tool call valid metric.
tool_name_match_input	ToolNameMatchInput Input for tool name match metric.
tool_parameter_key_match_input	ToolParameterKeyMatchInput Input for tool parameter key match metric.
tool_parameter_kv_match_input	ToolParameterKVMatchInput Input for tool parameter key value match metric.
comet_input	CometInput Translation metrics. Input for Comet metric.
metricx_input	MetricxInput Input for Metricx metric.
trajectory_exact_match_input	TrajectoryExactMatchInput Input for trajectory exact match metric.
trajectory_in_order_match_input	TrajectoryInOrderMatchInput Input for trajectory in order match metric.
trajectory_any_order_match_input	TrajectoryAnyOrderMatchInput Input for trajectory match any order metric.
trajectory_precision_input	TrajectoryPrecisionInput Input for trajectory precision metric.
trajectory_recall_input	TrajectoryRecallInput Input for trajectory recall metric.
trajectory_single_tool_use_input	TrajectorySingleToolUseInput Input for trajectory single tool use metric.
rubric_based_instruction_following_input	RubricBasedInstructionFollowingInput Rubric Based Instruction Following metric.

EvaluateInstancesResponse

Response message for EvaluationService.EvaluateInstances.

Fields
metric_results[]	MetricResult Metric results for each instance. The order of the metric results is guaranteed to be the same as the order of the instances in the request.
Union field evaluation_results. Evaluation results will be served in the same order as presented in EvaluationRequest.instances. evaluation_results can be only one of the following:
exact_match_results	ExactMatchResults Auto metric evaluation results. Results for exact match metric.
bleu_results	BleuResults Results for bleu metric.
rouge_results	RougeResults Results for rouge metric.
fluency_result	FluencyResult LLM-based metric evaluation result. General text generation metrics, applicable to other categories. Result for fluency metric.
coherence_result	CoherenceResult Result for coherence metric.
safety_result	SafetyResult Result for safety metric.
groundedness_result	GroundednessResult Result for groundedness metric.
fulfillment_result	FulfillmentResult Result for fulfillment metric.
summarization_quality_result	SummarizationQualityResult Summarization only metrics. Result for summarization quality metric.
pairwise_summarization_quality_result	PairwiseSummarizationQualityResult Result for pairwise summarization quality metric.
summarization_helpfulness_result	SummarizationHelpfulnessResult Result for summarization helpfulness metric.
summarization_verbosity_result	SummarizationVerbosityResult Result for summarization verbosity metric.
question_answering_quality_result	QuestionAnsweringQualityResult Question answering only metrics. Result for question answering quality metric.
pairwise_question_answering_quality_result	PairwiseQuestionAnsweringQualityResult Result for pairwise question answering quality metric.
question_answering_relevance_result	QuestionAnsweringRelevanceResult Result for question answering relevance metric.
question_answering_helpfulness_result	QuestionAnsweringHelpfulnessResult Result for question answering helpfulness metric.
question_answering_correctness_result	QuestionAnsweringCorrectnessResult Result for question answering correctness metric.
pointwise_metric_result	PointwiseMetricResult Generic metrics. Result for pointwise metric.
pairwise_metric_result	PairwiseMetricResult Result for pairwise metric.
tool_call_valid_results	ToolCallValidResults Tool call metrics. Results for tool call valid metric.
tool_name_match_results	ToolNameMatchResults Results for tool name match metric.
tool_parameter_key_match_results	ToolParameterKeyMatchResults Results for tool parameter key match metric.
tool_parameter_kv_match_results	ToolParameterKVMatchResults Results for tool parameter key value match metric.
comet_result	CometResult Translation metrics. Result for Comet metric.
metricx_result	MetricxResult Result for Metricx metric.
trajectory_exact_match_results	TrajectoryExactMatchResults Result for trajectory exact match metric.
trajectory_in_order_match_results	TrajectoryInOrderMatchResults Result for trajectory in order match metric.
trajectory_any_order_match_results	TrajectoryAnyOrderMatchResults Result for trajectory any order match metric.
trajectory_precision_results	TrajectoryPrecisionResults Result for trajectory precision metric.
trajectory_recall_results	TrajectoryRecallResults Results for trajectory recall metric.
trajectory_single_tool_use_results	TrajectorySingleToolUseResults Results for trajectory single tool use metric.
rubric_based_instruction_following_result	RubricBasedInstructionFollowingResult Result for rubric based instruction following metric.

EvaluatedAnnotation

True positive, false positive, or false negative.

EvaluatedAnnotation is only available under ModelEvaluationSlice with slice of annotationSpec dimension.

Fields
type	EvaluatedAnnotationType Output only. Type of the EvaluatedAnnotation.
predictions[]	Value Output only. The model predicted annotations. For true positive, there is one and only one prediction, which matches the only one ground truth annotation in ground_truths. For false positive, there is one and only one prediction, which doesn't match any ground truth annotation of the corresponding data_item_view_id. For false negative, there are zero or more predictions which are similar to the only ground truth annotation in ground_truths but not enough for a match. The schema of the prediction is stored in [ModelEvaluation.annotation_schema_uri][]
ground_truths[]	Value Output only. The ground truth Annotations, i.e. the Annotations that exist in the test data the Model is evaluated on. For true positive, there is one and only one ground truth annotation, which matches the only prediction in predictions. For false positive, there are zero or more ground truth annotations that are similar to the only prediction in predictions, but not enough for a match. For false negative, there is one and only one ground truth annotation, which doesn't match any predictions created by the model. The schema of the ground truth is stored in [ModelEvaluation.annotation_schema_uri][]
data_item_payload	Value Output only. The data item payload that the Model predicted this EvaluatedAnnotation on.
evaluated_data_item_view_id	string Output only. ID of the EvaluatedDataItemView under the same ancestor ModelEvaluation. The EvaluatedDataItemView consists of all ground truths and predictions on data_item_payload.
explanations[]	EvaluatedAnnotationExplanation Explanations of predictions. Each element of the explanations indicates the explanation for one explanation Method. The attributions list in the EvaluatedAnnotationExplanation.explanation object corresponds to the predictions list. For example, the second element in the attributions list explains the second element in the predictions list.
error_analysis_annotations[]	ErrorAnalysisAnnotation Annotations of model error analysis results.

EvaluatedAnnotationType

Describes the type of the EvaluatedAnnotation. The type is determined

Enums
EVALUATED_ANNOTATION_TYPE_UNSPECIFIED	Invalid value.
TRUE_POSITIVE	The EvaluatedAnnotation is a true positive. It has a prediction created by the Model and a ground truth Annotation which the prediction matches.
FALSE_POSITIVE	The EvaluatedAnnotation is false positive. It has a prediction created by the Model which does not match any ground truth annotation.
FALSE_NEGATIVE	The EvaluatedAnnotation is false negative. It has a ground truth annotation which is not matched by any of the model created predictions.

EvaluatedAnnotationExplanation

Explanation result of the prediction produced by the Model.

Fields
explanation_type	string Explanation type. For AutoML Image Classification models, possible values are: image-integrated-gradients image-xrai
explanation	Explanation Explanation attribution response details.

EvaluationConfig

Evaluation Config for Tuning Job.

Fields
metrics[]	Metric Required. The metrics used for evaluation.
output_config	OutputConfig Required. Config for evaluation output.
autorater_config	AutoraterConfig Optional. Autorater config for evaluation.
inference_generation_config	GenerationConfig Optional. Configuration options for inference generation and outputs. If not set, default generation parameters are used.

EvaluationDataset

The dataset used for evaluation.

Fields
Union field source. The source of the dataset. source can be only one of the following:
gcs_source	GcsSource Cloud storage source holds the dataset. Currently only one Cloud Storage file path is supported.
bigquery_source	BigQuerySource BigQuery source holds the dataset.

EvaluationInstance

A single instance to be evaluated. Instances are used to specify the input data for evaluation, from simple string comparisons to complex, multi-turn model evaluations

Fields
prompt	InstanceData Optional. Data used to populate placeholder prompt in a metric prompt template.
rubric_groups	map<string, RubricGroup> Optional. Named groups of rubrics associated with the prompt. This is used for rubric-based evaluations where rubrics can be referenced by a key. The key could represent versions, associated metrics, etc.
response	InstanceData Optional. Data used to populate placeholder response in a metric prompt template.
reference	InstanceData Optional. Data used to populate placeholder reference in a metric prompt template.
other_data	MapInstance Optional. Other data used to populate placeholders based on their key. If a key conflicts with a field in the EvaluationInstance (e.g. prompt), the value of the field will take precedence over the value in other_data.
agent_data (deprecated)	DeprecatedAgentData This item is deprecated! Optional. Deprecated: Use agent_eval_data instead. Data used for agent evaluation.
agent_eval_data	AgentData Optional. Data used for agent evaluation.

DeprecatedAgentConfig

This item is deprecated!

Deprecated: Use google.cloud.aiplatform.master.AgentConfig in agent_eval_data instead. Configuration for an Agent.

Fields
agent_id	string Optional. Unique identifier of the agent. This ID is used to refer to this agent, e.g., in AgentEvent.author, or in the sub_agents field. It must be unique within the agents map.
agent_type	string Optional. The type or class of the agent (e.g., "LlmAgent", "RouterAgent", "ToolUseAgent"). Useful for the autorater to understand the expected behavior of the agent.
description	string Optional. A high-level description of the agent's role and responsibilities. Critical for evaluating if the agent is routing tasks correctly.
sub_agents[]	string Optional. The list of valid agent IDs (names) that this agent can delegate to. This defines the directed edges in the agent system graph topology.
developer_instruction	InstanceData Optional. Contains instructions from the developer for the agent. Can be static or a dynamic prompt template used with the AgentEvent.state_delta field.
Union field tools_data. Data for the tools available to the agent. tools_data can be only one of the following:
tools_text	string A JSON string containing a list of tools available to an agent with info such as name, description, parameters and required parameters.
tools	Tools List of tools.

Tools

Represents a list of tools for an agent.

Fields
tool[]	Tool Optional. List of tools: each tool can have multiple function declarations.

DeprecatedAgentData

This item is deprecated!

Deprecated: Use agent_eval_data instead. Contains data specific to agent evaluations.

Fields
agents	map<string, DeprecatedAgentConfig> Optional. The static Agent Configuration. This map defines the graph structure of the agent system. Key: agent_id (matches the author field in events). Value: The static configuration of the agent (tools, instructions, sub-agents).
turns[]	ConversationTurn Optional. The chronological list of conversation turns. Each turn represents a logical execution cycle (e.g., User Input -> Agent Response).
developer_instruction (deprecated)	InstanceData This item is deprecated! Optional. Deprecated: Use agents.developer_instruction or turns.events.active_instruction instead. A field containing instructions from the developer for the agent.
agent_config	DeprecatedAgentConfig Optional. Deprecated: Use agent_eval_data instead. Agent configuration.
Union field tools_data. --- Legacy fields below. To be deprecated. --- Deprecated: Use agents instead. Data for the tools available to the agent. tools_data can be only one of the following:
tools_text (deprecated)	string This item is deprecated! A JSON string containing a list of tools available to an agent with info such as name, description, parameters and required parameters.
tools (deprecated)	Tools This item is deprecated! List of tools.
Union field events_data. The sequence of function calls and function responses that form the agent's trajectory. events_data can be only one of the following:
events	Events A list of events.

AgentEvent

A single event in the execution trace.

Fields
content	Content Required. The content of the event (e.g., text response, tool call, tool response).
event_time	Timestamp Optional. The timestamp when the event occurred.
state_delta	Struct Optional. The change in the session state caused by this event. This is a key-value map of fields that were modified or added by the event.
active_tools[]	Tool Optional. The list of tools that were active/available to the agent at the time of this event. This overrides the AgentConfig.tools if set.
author	string Required. The ID of the agent or entity that generated this event.

ConversationTurn

Represents a single turn/invocation in the conversation.

Fields
turn_id	string Optional. A unique identifier for the turn. Useful for referencing specific turns across systems.
events[]	AgentEvent Optional. The list of events that occurred during this turn.
turn_index	int32 Required. The 0-based index of the turn in the conversation sequence.

Events

Represents a list of events for an agent.

Fields
event[]	Content Optional. A list of events.

Tools

Deprecated: Use agent_eval_data instead. Represents a list of tools for an agent.

Fields
tool[] (deprecated)	Tool This item is deprecated! Optional. List of tools: each tool can have multiple function declarations.

InstanceData

Instance data used to populate placeholders in a metric prompt template.

Fields
Union field data. Supported formats for instance data. data can be only one of the following:
text	string Text data.
contents	Contents List of Gemini content data.

Contents

List of standard Content messages from Gemini API.

Fields
contents[]	Content Optional. Repeated contents.

MapInstance

Instance data specified as a map.

Fields
map_instance	map<string, InstanceData> Optional. Map of instance data.

EvaluationParserConfig

Config for parsing LLM responses. It can be used to parse the LLM response to be evaluated, or the LLM response from LLM-based metrics/Autoraters.

Fields
Union field parser. The parser to use. parser can be only one of the following:
custom_code_parser_config	CustomCodeParserConfig Optional. Use custom code to parse the LLM response.

CustomCodeParserConfig

Configuration for parsing the LLM response using custom code.

Fields

parsing_function

string Required. Python function for parsing results. The function should be defined within this string. The function takes a list of strings (LLM responses) and should return either a list of dictionaries (for rubrics) or a single dictionary (for a metric result). Example function signature: def parse(responses: list[str]) -> list[dict[str, Any]] | dict[str, Any]: When parsing rubrics, return a list of dictionaries, where each dictionary represents a Rubric. Example for rubrics: [ { "content": {"property": {"description": "The response is factual."}}, "type": "FACTUALITY", "importance": "HIGH" }, { "content": {"property": {"description": "The response is fluent."}}, "type": "FLUENCY", "importance": "MEDIUM" } ] When parsing critique results, return a dictionary representing a MetricResult. Example for a metric result: { "score": 0.8, "explanation": "The model followed most instructions.", "rubric_verdicts": [...] } ... code for result extraction and aggregation

Event

An edge describing the relationship between an Artifact and an Execution in a lineage graph.

Fields
artifact	string Required. The relative resource name of the Artifact in the Event.
execution	string Output only. The relative resource name of the Execution in the Event.
event_time	Timestamp Output only. Time the Event occurred.
type	Type Required. The type of the Event.
labels	map<string, string> The labels with user-defined metadata to annotate Events. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Event (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.

Type

Describes whether an Event's Artifact is the Execution's input or output.

Enums
TYPE_UNSPECIFIED	Unspecified whether input or output of the Execution.
INPUT	An input of the Execution.
OUTPUT	An output of the Execution.

EventActions

Actions are parts of events that are executed by the agent.

Fields
skip_summarization	bool Optional. If true, it won't call model to summarize function response. Only used for function_response event.
state_delta	Struct Optional. Indicates that the event is updating the state with the given delta.
artifact_delta	map<string, int32> Optional. Indicates that the event is updating an artifact. key is the filename, value is the version.
escalate	bool Optional. The agent is escalating to a higher level agent.
requested_auth_configs	Struct Optional. Will only be set by a tool response indicating tool request euc. Struct key is the function call id since one function call response (from model) could correspond to multiple function calls. Struct value is the required auth config, which can be another struct.
transfer_agent	string Optional. If set, the event transfers to the specified agent.

EventMetadata

Metadata relating to a LLM response event.

Fields
grounding_metadata	GroundingMetadata Optional. Metadata returned to client when grounding is enabled.
partial	bool Optional. Indicates whether the text content is part of a unfinished text stream. Only used for streaming mode and when the content is plain text.
turn_complete	bool Optional. Indicates whether the response from the model is complete. Only used for streaming mode.
interrupted	bool Optional. Flag indicating that LLM was interrupted when generating the content. Usually it's due to user interruption during a bidi streaming.
long_running_tool_ids[]	string Optional. Set of ids of the long running function calls. Agent client will know from this field about which function call is long running. Only valid for function call event.
branch	string Optional. The branch of the event. The format is like agent_1.agent_2.agent_3, where agent_1 is the parent of agent_2, and agent_2 is the parent of agent_3. Branch is used when multiple child agents shouldn't see their siblings' conversation history.
custom_metadata	Struct The custom metadata of the LlmResponse.
input_transcription	Transcription Optional. Audio transcription of user input.
output_transcription	Transcription Optional. Audio transcription of model output.

ExactMatchInput

Input for exact match metric.

Fields
metric_spec	ExactMatchSpec Required. Spec for exact match metric.
instances[]	ExactMatchInstance Required. Repeated exact match instances.

ExactMatchInstance

Spec for exact match instance.

Fields
prediction	string Required. Output of the evaluated model.
reference	string Required. Ground truth used to compare against the prediction.

ExactMatchMetricValue

Exact match metric value for an instance.

Fields
score	float Output only. Exact match score.

ExactMatchResults

Results for exact match metric.

Fields
exact_match_metric_values[]	ExactMatchMetricValue Output only. Exact match metric values.

ExactMatchSpec

This type has no fields.

Spec for exact match metric - returns 1 if prediction and reference exactly matches, otherwise 0.

Example

Fields
display_name	string Optional. The display name for Example.
example_id	string Optional. Immutable. Unique identifier of an example. If not specified when upserting new examples, the example_id will be generated.
create_time	Timestamp Output only. Timestamp when this Example was created.
Union field example_type. The type of the example. Each example type has a defined format example_type can be only one of the following:
stored_contents_example	StoredContentsExample An example of chat history and its expected outcome to be used with GenerateContent.

ExampleStore

Represents an executable service to manage and retrieve examples.

Fields
name	string Identifier. The resource name of the ExampleStore. This is a unique identifier. Format: projects/{project}/locations/{location}/exampleStores/{example_store}
display_name	string Required. Display name of the ExampleStore.
description	string Optional. Description of the ExampleStore.
create_time	Timestamp Output only. Timestamp when this ExampleStore was created.
update_time	Timestamp Output only. Timestamp when this ExampleStore was most recently updated.
example_store_config	ExampleStoreConfig Required. Example Store config.

ExampleStoreConfig

Configuration for the Example Store.

Fields
vertex_embedding_model	string Required. The embedding model to be used for vector embedding. Immutable. Supported models: * "text-embedding-005" * "text-multilingual-embedding-002"

Examples

Example-based explainability that returns the nearest neighbors from the provided dataset.

Fields
gcs_source	GcsSource The Cloud Storage locations that contain the instances to be indexed for approximate nearest neighbor search.
neighbor_count	int32 The number of neighbors to return when querying for examples.
Union field source. source can be only one of the following:
example_gcs_source	ExampleGcsSource The Cloud Storage input instances.
Union field config. config can be only one of the following:
nearest_neighbor_search_config	Value The full configuration for the generated index, the semantics are the same as metadata and should match NearestNeighborSearchConfig.
presets	Presets Simplified preset configuration, which automatically sets configuration values based on the desired query speed-precision trade-off and modality.

ExampleGcsSource

The Cloud Storage input instances.

Fields
data_format	DataFormat The format in which instances are given, if not specified, assume it's JSONL format. Currently only JSONL format is supported.
gcs_source	GcsSource The Cloud Storage location for the input instances.

DataFormat

The format of the input example instances.

Enums
DATA_FORMAT_UNSPECIFIED	Format unspecified, used when unset.
JSONL	Examples are stored in JSONL files.

ExamplesArrayFilter

Filters for examples' array metadata fields. An array field is example metadata where multiple values are attributed to a single example.

Fields
values[]	string Required. The values by which to filter examples.
array_operator	ArrayOperator Required. The operator logic to use for filtering.

ArrayOperator

The logic to use for filtering.

Enums
ARRAY_OPERATOR_UNSPECIFIED	Not specified. This value should not be used.
CONTAINS_ANY	The metadata array field in the example must contain at least one of the values.
CONTAINS_ALL	The metadata array field in the example must contain all of the values.

ExamplesOverride

Overrides for example-based explanations.

Fields
neighbor_count	int32 The number of neighbors to return.
crowding_count	int32 The number of neighbors to return that have the same crowding tag.
restrictions[]	ExamplesRestrictionsNamespace Restrict the resulting nearest neighbors to respect these constraints.
return_embeddings	bool If true, return the embeddings instead of neighbors.
data_format	DataFormat The format of the data being provided with each call.

DataFormat

Data format enum.

Enums
DATA_FORMAT_UNSPECIFIED	Unspecified format. Must not be used.
INSTANCES	Provided data is a set of model inputs.
EMBEDDINGS	Provided data is a set of embeddings.

ExamplesRestrictionsNamespace

Restrictions namespace for example-based explanations overrides.

Fields
namespace_name	string The namespace name.
allow[]	string The list of allowed tags.
deny[]	string The list of deny tags.

ExecutableCode

Code generated by the model that is meant to be executed, and the result returned to the model.

Generated when using the CodeExecution tool, in which the code will be automatically executed, and a corresponding CodeExecutionResult will also be generated.

Fields
language	Language Required. Programming language of the code.
code	string Required. The code to be executed.

Language

Supported programming languages for the generated code.

Enums
LANGUAGE_UNSPECIFIED	Unspecified language. This value should not be used.
PYTHON	Python >= 3.10, with numpy and simpy available.

ExecuteExtensionRequest

Request message for ExtensionExecutionService.ExecuteExtension.

Fields
name	string Required. Name (identifier) of the extension; Format: projects/{project}/locations/{location}/extensions/{extension}
operation_id	string Required. The desired ID of the operation to be executed in this extension as defined in ExtensionOperation.operation_id.
operation_params	Struct Optional. Request parameters that will be used for executing this operation. The struct should be in a form of map with param name as the key and actual param value as the value. E.g. If this operation requires a param "name" to be set to "abc". you can set this to something like {"name": "abc"}.
runtime_auth_config	AuthConfig Optional. Auth config provided at runtime to override the default value in [Extension.manifest.auth_config][]. The AuthConfig.auth_type should match the value in [Extension.manifest.auth_config][].

ExecuteExtensionResponse

Response message for ExtensionExecutionService.ExecuteExtension.

Fields
content	string Response content from the extension. The content should be conformant to the response.content schema in the extension's manifest/OpenAPI spec.

Execution

Instance of a general execution.

Fields
name	string Output only. The resource name of the Execution.
display_name	string User provided display name of the Execution. May be up to 128 Unicode characters.
state	State The state of this Execution. This is a property of the Execution, and does not imply or capture any ongoing process. This property is managed by clients (such as Agent Platform Pipelines) and the system does not prescribe or check the validity of state transitions.
etag	string An eTag used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map<string, string> The labels with user-defined metadata to organize your Executions. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. No more than 64 user labels can be associated with one Execution (System labels are excluded).
create_time	Timestamp Output only. Timestamp when this Execution was created.
update_time	Timestamp Output only. Timestamp when this Execution was last updated.
schema_title	string The title of the schema describing the metadata. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store.
schema_version	string The version of the schema in schema_title to use. Schema title and version is expected to be registered in earlier Create Schema calls. And both are used together as unique identifiers to identify schemas within the local metadata store.
metadata	Struct Properties of the Execution. Top level metadata keys' heading and trailing spaces will be trimmed. The size of this field should not exceed 200KB.
description	string Description of the Execution

State

Describes the state of the Execution.

Enums
STATE_UNSPECIFIED	Unspecified Execution state
NEW	The Execution is new
RUNNING	The Execution is running
COMPLETE	The Execution has finished running
FAILED	The Execution has failed
CACHED	The Execution completed through Cache hit.
CANCELLED	The Execution was cancelled.

ExplainRequest

Request message for PredictionService.Explain.

Fields
endpoint	string Required. The name of the Endpoint requested to serve the explanation. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
instances[]	Value Required. The instances that are the input to the explanation call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the explanation call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.
parameters	Value The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' Model's PredictSchemata's parameters_schema_uri.
explanation_spec_override	ExplanationSpecOverride If specified, overrides the explanation_spec of the DeployedModel. Can be used for explaining prediction results with different configurations, such as: - Explaining top-5 predictions results as opposed to top-1; - Increasing path count or step count of the attribution methods to reduce approximate errors; - Using different baselines for explaining the prediction results.
concurrent_explanation_spec_override	map<string, ExplanationSpecOverride> Optional. This field is the same as the one above, but supports multiple explanations to occur in parallel. The key can be any string. Each override will be run against the model, then its explanations will be grouped together. Note - these explanations are run In Addition to the default Explanation in the deployed model.
deployed_model_id	string If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding Endpoint.traffic_split.

ExplainResponse

Response message for PredictionService.Explain.

Fields
explanations[]	Explanation The explanations of the Model's PredictResponse.predictions. It has the same number of elements as instances to be explained.
concurrent_explanations	map<string, ConcurrentExplanation> This field stores the results of the explanations run in parallel with The default explanation strategy/method.
deployed_model_id	string ID of the Endpoint's DeployedModel that served this explanation.
predictions[]	Value The predictions that are the output of the predictions call. Same as PredictResponse.predictions.

ConcurrentExplanation

This message is a wrapper grouping Concurrent Explanations.

Fields
explanations[]	Explanation The explanations of the Model's PredictResponse.predictions. It has the same number of elements as instances to be explained.

Explanation

Explanation of a prediction (provided in PredictResponse.predictions) produced by the Model on a given instance.

Fields
attributions[]	Attribution Output only. Feature attributions grouped by predicted outputs. For Models that predict only one output, such as regression Models that predict only one score, there is only one attibution that explains the predicted output. For Models that predict multiple outputs, such as multiclass Models that predict multiple classes, each element explains one specific item. Attribution.output_index can be used to identify which output this attribution is explaining. By default, we provide Shapley values for the predicted class. However, you can configure the explanation request to generate Shapley values for any other classes too. For example, if a model predicts a probability of 0.4 for approving a loan application, the model's decision is to reject the application since p(reject) = 0.6 > p(approve) = 0.4, and the default Shapley values would be computed for rejection decision and not approval, even though the latter might be the positive class. If users set ExplanationParameters.top_k, the attributions are sorted by instance_output_value in descending order. If ExplanationParameters.output_indices is specified, the attributions are stored by Attribution.output_index in the same order as they appear in the output_indices.
neighbors[]	Neighbor Output only. List of the nearest neighbors for example-based explanations. For models deployed with the examples explanations feature enabled, the attributions field is empty and instead the neighbors field is populated.

ExplanationMetadata

Metadata describing the Model's input and output for explanation.

Fields
inputs	map<string, InputMetadata> Required. Map from feature names to feature input metadata. Keys are the name of the features. Values are the specification of the feature. An empty InputMetadata is valid. It describes a text feature which has the name specified as the key in ExplanationMetadata.inputs. The baseline of the empty feature is chosen by Agent Platform. For Agent Platform-provided Tensorflow images, the key can be any friendly name of the feature. Once specified, featureAttributions are keyed by this key (if not grouped with another feature). For custom images, the key must match with the key in instance.
outputs	map<string, OutputMetadata> Required. Map from output names to output metadata. For Agent Platform-provided Tensorflow images, keys can be any user defined string that consists of any UTF-8 characters. For custom images, keys are the name of the output field in the prediction to be explained. Currently only one key is allowed.
feature_attributions_schema_uri	string Points to a YAML file stored on Google Cloud Storage describing the format of the feature attributions. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML tabular Models always have this field populated by Agent Platform. Note: The URI given on output may be different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
latent_space_source	string Name of the source to generate embeddings for example based explanations.

InputMetadata

Metadata of the input of a feature.

Fields other than InputMetadata.input_baselines are applicable only for Models that are using Agent Platform-provided images for Tensorflow.

Fields
input_baselines[]	Value Baseline inputs for this feature. If no baseline is specified, Agent Platform chooses the baseline for this feature. If multiple baselines are specified, Agent Platform returns the average attributions across them in Attribution.feature_attributions. For Agent Platform-provided Tensorflow images (both 1.x and 2.x), the shape of each baseline must match the shape of the input tensor. If a scalar is provided, we broadcast to the same shape as the input tensor. For custom images, the element of the baselines must be in the same format as the feature's input in the instance[]. The schema of any single instance may be specified via Endpoint's DeployedModels' Model's PredictSchemata's instance_schema_uri.
input_tensor_name	string Name of the input tensor for this feature. Required and is only applicable to Agent Platform-provided images for Tensorflow.
encoding	Encoding Defines how the feature is encoded into the input tensor. Defaults to IDENTITY.
modality	string Modality of the feature. Valid values are: numeric, image. Defaults to numeric.
feature_value_domain	FeatureValueDomain The domain details of the input feature value. Like min/max, original mean or standard deviation if normalized.
indices_tensor_name	string Specifies the index of the values of the input tensor. Required when the input tensor is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
dense_shape_tensor_name	string Specifies the shape of the values of the input if the input is a sparse representation. Refer to Tensorflow documentation for more details: https://www.tensorflow.org/api_docs/python/tf/sparse/SparseTensor.
index_feature_mapping[]	string A list of feature names for each index in the input tensor. Required when the input InputMetadata.encoding is BAG_OF_FEATURES, BAG_OF_FEATURES_SPARSE, INDICATOR.
encoded_tensor_name	string Encoded tensor is a transformation of the input tensor. Must be provided if choosing Integrated Gradients attribution or XRAI attribution and the input tensor is not differentiable. An encoded tensor is generated if the input tensor is encoded by a lookup table.
encoded_baselines[]	Value A list of baselines for the encoded tensor. The shape of each baseline should match the shape of the encoded tensor. If a scalar is provided, Agent Platform broadcasts to the same shape as the encoded tensor.
visualization	Visualization Visualization configurations for image explanation.
group_name	string Name of the group that the input belongs to. Features with the same group name will be treated as one feature when computing attributions. Features grouped together can have different shapes in value. If provided, there will be one single attribution generated in Attribution.feature_attributions, keyed by the group name.

Encoding

Defines how a feature is encoded. Defaults to IDENTITY.

Enums
ENCODING_UNSPECIFIED	Default value. This is the same as IDENTITY.
IDENTITY	The tensor represents one feature.
BAG_OF_FEATURES	The tensor represents a bag of features where each index maps to a feature. InputMetadata.index_feature_mapping must be provided for this encoding. For example: input = [27, 6.0, 150] index_feature_mapping = ["age", "height", "weight"]
BAG_OF_FEATURES_SPARSE	The tensor represents a bag of features where each index maps to a feature. Zero values in the tensor indicates feature being non-existent. InputMetadata.index_feature_mapping must be provided for this encoding. For example: input = [2, 0, 5, 0, 1] index_feature_mapping = ["a", "b", "c", "d", "e"]
INDICATOR	The tensor is a list of binaries representing whether a feature exists or not (1 indicates existence). InputMetadata.index_feature_mapping must be provided for this encoding. For example: input = [1, 0, 1, 0, 1] index_feature_mapping = ["a", "b", "c", "d", "e"]
COMBINED_EMBEDDING	The tensor is encoded into a 1-dimensional array represented by an encoded tensor. InputMetadata.encoded_tensor_name must be provided for this encoding. For example: input = ["This", "is", "a", "test", "."] encoded = [0.1, 0.2, 0.3, 0.4, 0.5]
CONCAT_EMBEDDING	Select this encoding when the input tensor is encoded into a 2-dimensional array represented by an encoded tensor. InputMetadata.encoded_tensor_name must be provided for this encoding. The first dimension of the encoded tensor's shape is the same as the input tensor's shape. For example: input = ["This", "is", "a", "test", "."] encoded = [[0.1, 0.2, 0.3, 0.4, 0.5], [0.2, 0.1, 0.4, 0.3, 0.5], [0.5, 0.1, 0.3, 0.5, 0.4], [0.5, 0.3, 0.1, 0.2, 0.4], [0.4, 0.3, 0.2, 0.5, 0.1]]

FeatureValueDomain

Domain details of the input feature value. Provides numeric information about the feature, such as its range (min, max). If the feature has been pre-processed, for example with z-scoring, then it provides information about how to recover the original feature. For example, if the input feature is an image and it has been pre-processed to obtain 0-mean and stddev = 1 values, then original_mean, and original_stddev refer to the mean and stddev of the original feature (e.g. image tensor) from which input feature (with mean = 0 and stddev = 1) was obtained.

Fields
min_value	float The minimum permissible value for this feature.
max_value	float The maximum permissible value for this feature.
original_mean	float If this input feature has been normalized to a mean value of 0, the original_mean specifies the mean value of the domain prior to normalization.
original_stddev	float If this input feature has been normalized to a standard deviation of 1.0, the original_stddev specifies the standard deviation of the domain prior to normalization.

Visualization

Visualization configurations for image explanation.

Fields
type	Type Type of the image visualization. Only applicable to Integrated Gradients attribution. OUTLINES shows regions of attribution, while PIXELS shows per-pixel attribution. Defaults to OUTLINES.
polarity	Polarity Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE.
color_map	ColorMap The color scheme used for the highlighted areas. Defaults to PINK_GREEN for Integrated Gradients attribution, which shows positive attributions in green and negative in pink. Defaults to VIRIDIS for XRAI attribution, which highlights the most influential regions in yellow and the least influential in blue.
clip_percent_upperbound	float Excludes attributions above the specified percentile from the highlighted areas. Using the clip_percent_upperbound and clip_percent_lowerbound together can be useful for filtering out noise and making it easier to see areas of strong attribution. Defaults to 99.9.
clip_percent_lowerbound	float Excludes attributions below the specified percentile, from the highlighted areas. Defaults to 62.
overlay_type	OverlayType How the original image is displayed in the visualization. Adjusting the overlay can help increase visual clarity if the original image makes it difficult to view the visualization. Defaults to NONE.

ColorMap

The color scheme used for highlighting areas.

Enums
COLOR_MAP_UNSPECIFIED	Should not be used.
PINK_GREEN	Positive: green. Negative: pink.
VIRIDIS	Viridis color map: A perceptually uniform color mapping which is easier to see by those with colorblindness and progresses from yellow to green to blue. Positive: yellow. Negative: blue.
RED	Positive: red. Negative: red.
GREEN	Positive: green. Negative: green.
RED_GREEN	Positive: green. Negative: red.
PINK_WHITE_GREEN	PiYG palette.

OverlayType

How the original image is displayed in the visualization.

Enums
OVERLAY_TYPE_UNSPECIFIED	Default value. This is the same as NONE.
NONE	No overlay.
ORIGINAL	The attributions are shown on top of the original image.
GRAYSCALE	The attributions are shown on top of grayscaled version of the original image.
MASK_BLACK	The attributions are used as a mask to reveal predictive parts of the image and hide the un-predictive parts.

Polarity

Whether to only highlight pixels with positive contributions, negative or both. Defaults to POSITIVE.

Enums
POLARITY_UNSPECIFIED	Default value. This is the same as POSITIVE.
POSITIVE	Highlights the pixels/outlines that were most influential to the model's prediction.
NEGATIVE	Setting polarity to negative highlights areas that does not lead to the models's current prediction.
BOTH	Shows both positive and negative attributions.

Type

Type of the image visualization. Only applicable to Integrated Gradients attribution.

Enums
TYPE_UNSPECIFIED	Should not be used.
PIXELS	Shows which pixel contributed to the image prediction.
OUTLINES	Shows which region contributed to the image prediction by outlining the region.

OutputMetadata

Metadata of the prediction output to be explained.

Fields
output_tensor_name	string Name of the output tensor. Required and is only applicable to Agent Platform provided images for Tensorflow.
Union field display_name_mapping. Defines how to map Attribution.output_index to Attribution.output_display_name. If neither of the fields are specified, Attribution.output_display_name will not be populated. display_name_mapping can be only one of the following:
index_display_name_mapping	Value Static mapping between the index and display name. Use this if the outputs are a deterministic n-dimensional array, e.g. a list of scores of all the classes in a pre-defined order for a multi-classification Model. It's not feasible if the outputs are non-deterministic, e.g. the Model produces top-k classes or sort the outputs by their values. The shape of the value must be an n-dimensional array of strings. The number of dimensions must match that of the outputs to be explained. The Attribution.output_display_name is populated by locating in the mapping with Attribution.output_index.
display_name_mapping_key	string Specify a field name in the prediction to look for the display name. Use this if the prediction contains the display names for the outputs. The display names in the prediction must have the same shape of the outputs, so that it can be located by Attribution.output_index for a specific output.

ExplanationMetadataOverride

The ExplanationMetadata entries that can be overridden at online explanation time.

Fields
inputs	map<string, InputMetadataOverride> Required. Overrides the input metadata of the features. The key is the name of the feature to be overridden. The keys specified here must exist in the input metadata to be overridden. If a feature is not specified here, the corresponding feature's input metadata is not overridden.

InputMetadataOverride

The input metadata entries to be overridden.

Fields
input_baselines[]	Value Baseline inputs for this feature. This overrides the input_baseline field of the ExplanationMetadata.InputMetadata object of the corresponding feature's input metadata. If it's not specified, the original baselines are not overridden.

ExplanationParameters

Parameters to configure explaining for Model's predictions.

Fields
top_k	int32 If populated, returns attributions for top K indices of outputs (defaults to 1). Only applies to Models that predicts more than one outputs (e,g, multi-class Models). When set to -1, returns explanations for all outputs.
output_indices	ListValue If populated, only returns attributions that have output_index contained in output_indices. It must be an ndarray of integers, with the same shape of the output it's explaining. If not populated, returns attributions for top_k indices of outputs. If neither top_k nor output_indices is populated, returns the argmax index of the outputs. Only applicable to Models that predict multiple outputs (e,g, multi-class Models that predict multiple classes).
Union field method. method can be only one of the following:
sampled_shapley_attribution	SampledShapleyAttribution An attribution method that approximates Shapley values for features that contribute to the label being predicted. A sampling strategy is used to approximate the value rather than considering all subsets of features. Refer to this paper for model details: https://arxiv.org/abs/1306.4265.
integrated_gradients_attribution	IntegratedGradientsAttribution An attribution method that computes Aumann-Shapley values taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365
xrai_attribution	XraiAttribution An attribution method that redistributes Integrated Gradients attribution to segmented regions, taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1906.02825 XRAI currently performs better on natural images, like a picture of a house or an animal. If the images are taken in artificial environments, like a lab or manufacturing line, or from diagnostic equipment, like x-rays or quality-control cameras, use Integrated Gradients instead.
examples	Examples Example-based explanations that returns the nearest neighbors from the provided dataset.

ExplanationSpec

Specification of Model explanation.

Fields
parameters	ExplanationParameters Required. Parameters that configure explaining of the Model's predictions.
metadata	ExplanationMetadata Optional. Metadata describing the Model's input and output for explanation.

ExplanationSpecOverride

The ExplanationSpec entries that can be overridden at online explanation time.

Fields
parameters	ExplanationParameters The parameters to be overridden. Note that the attribution method cannot be changed. If not specified, no parameter is overridden.
metadata	ExplanationMetadataOverride The metadata to be overridden. If not specified, no metadata is overridden.
examples_override	ExamplesOverride The example-based explanations parameter overrides.

ExportDataConfig

Describes what part of the Dataset is to be exported, the destination of the export and how to export.

Fields
annotations_filter	string An expression for filtering what part of the Dataset is to be exported. Only Annotations that match this filter will be exported. The filter syntax is the same as in ListAnnotations.
Union field destination. The destination of the output. destination can be only one of the following:
gcs_destination	GcsDestination The Google Cloud Storage location where the output is to be written to. In the given directory a new directory will be created with name: export-data-<dataset-display-name>-<timestamp-of-export-call> where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All export output will be written into that directory. Inside that directory, annotations with the same schema will be grouped into sub directories which are named with the corresponding annotations' schema title. Inside these sub directories, a schema.yaml will be created to describe the output format.
Union field split. The instructions how the export data should be split between the training, validation and test sets. split can be only one of the following:
fraction_split	ExportFractionSplit Split based on fractions defining the size of each set.

ExportDataOperationMetadata

Runtime operation information for DatasetService.ExportData.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.
gcs_output_directory	string A Google Cloud Storage directory which path ends with '/'. The exported data is stored in the directory.

ExportDataRequest

Request message for DatasetService.ExportData.

Fields
name	string Required. The name of the Dataset resource. Format: projects/{project}/locations/{location}/datasets/{dataset}
export_config	ExportDataConfig Required. The desired output location.

ExportDataResponse

Response message for DatasetService.ExportData.

Fields
exported_files[]	string All of the files that are exported in this export operation. For custom code training export, only three (training, validation and test) Cloud Storage paths in wildcard format are populated (for example, gs://.../training-*).

ExportFeatureValuesOperationMetadata

Details of operations that exports Features values.

Fields
generic_metadata	GenericOperationMetadata Operation metadata for Featurestore export Feature values.

ExportFeatureValuesRequest

Request message for FeaturestoreService.ExportFeatureValues.

Fields
entity_type	string Required. The resource name of the EntityType from which to export Feature values. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}
destination	FeatureValueDestination Required. Specifies destination location and format.
feature_selector	FeatureSelector Required. Selects Features to export values of.
settings[]	DestinationFeatureSetting Per-Feature export settings.
Union field mode. Required. The mode in which Feature values are exported. mode can be only one of the following:
snapshot_export	SnapshotExport Exports the latest Feature values of all entities of the EntityType within a time range.
full_export	FullExport Exports all historical values of all entities of the EntityType within a time range

FullExport

Describes exporting all historical Feature values of all entities of the EntityType between [start_time, end_time].

Fields
start_time	Timestamp Excludes Feature values with feature generation timestamp before this timestamp. If not set, retrieve oldest values kept in Feature Store. Timestamp, if present, must not have higher than millisecond precision.
end_time	Timestamp Exports Feature values as of this timestamp. If not set, retrieve values as of now. Timestamp, if present, must not have higher than millisecond precision.

SnapshotExport

Describes exporting the latest Feature values of all entities of the EntityType between [start_time, snapshot_time].

Fields
snapshot_time	Timestamp Exports Feature values as of this timestamp. If not set, retrieve values as of now. Timestamp, if present, must not have higher than millisecond precision.
start_time	Timestamp Excludes Feature values with feature generation timestamp before this timestamp. If not set, retrieve oldest values kept in Feature Store. Timestamp, if present, must not have higher than millisecond precision.

ExportFeatureValuesResponse

This type has no fields.

Response message for FeaturestoreService.ExportFeatureValues.

ExportFractionSplit

Assigns the input data to training, validation, and test sets as per the given fractions. Any of training_fraction, validation_fraction and test_fraction may optionally be provided, they must sum to up to 1. If the provided ones sum to less than 1, the remainder is assigned to sets as decided by Agent Platform. If none of the fractions are set, by default roughly 80% of data is used for training, 10% for validation, and 10% for test.

Fields
training_fraction	double The fraction of the input data that is to be used to train the Model.
validation_fraction	double The fraction of the input data that is to be used to validate the Model.
test_fraction	double The fraction of the input data that is to be used to evaluate the Model.

ExportModelOperationMetadata

Details of ModelService.ExportModel operation.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.
output_info	OutputInfo Output only. Information further describing the output of this Model export.

OutputInfo

Further describes the output of the ExportModel. Supplements ExportModelRequest.OutputConfig.

Fields
artifact_output_uri	string Output only. If the Model artifact is being exported to Google Cloud Storage this is the full path of the directory created, into which the Model files are being written to.
image_output_uri	string Output only. If the Model image is being exported to Google Artifact Registry this is the full path of the image created.

ExportModelRequest

Request message for ModelService.ExportModel.

Fields
name	string Required. The resource name of the Model to export. The resource name may contain version id or version alias to specify the version, if no version is specified, the default version will be exported.
output_config	OutputConfig Required. The desired output location and configuration.

OutputConfig

Output configuration for the Model export.

Fields
export_format_id	string The ID of the format in which the Model must be exported. Each Model lists the export formats it supports. If no value is provided here, then the first from the list of the Model's supported formats is used by default.
artifact_destination	GcsDestination The Cloud Storage location where the Model artifact is to be written to. Under the directory given as the destination a new one with name "model-export-<model-display-name>-<timestamp-of-export-call>", where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format, will be created. Inside, the Model and any of its supporting files will be written. This field should only be set when the exportableContent field of the [Model.supported_export_formats] object contains ARTIFACT.
image_destination	ContainerRegistryDestination The Google Artifact Registry uri where the Model container image will be copied to. This field should only be set when the exportableContent field of the [Model.supported_export_formats] object contains IMAGE.

ExportModelResponse

This type has no fields.

Response message of ModelService.ExportModel operation.

ExportPublisherModelOperationMetadata

Runtime operation information for ModelGardenService.ExportPublisherModel.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.

ExportPublisherModelRequest

Request message for ModelGardenService.ExportPublisherModel.

Fields
name	string Required. The name of the PublisherModel resource. Format: publishers/{publisher}/models/{publisher_model}@{version_id}, or publishers/hf-{hugging-face-author}/models/{hugging-face-model-name}@001
destination	GcsDestination Required. The target where we are exporting the model weights to
parent	string Required. The Location to export the model weights from Format: projects/{project}/locations/{location}

ExportPublisherModelResponse

Response message for ModelGardenService.ExportPublisherModel.

Fields
publisher_model	string The name of the PublisherModel resource. Format: publishers/{publisher}/models/{publisher_model}@{version_id}
destination_uri	string The destination uri of the model weights.

ExportTensorboardTimeSeriesDataRequest

Request message for TensorboardService.ExportTensorboardTimeSeriesData.

Fields
tensorboard_time_series	string Required. The resource name of the TensorboardTimeSeries to export data from. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}
filter	string Exports the TensorboardTimeSeries' data that match the filter expression.
page_size	int32 The maximum number of data points to return per page. The default page_size is 1000. Values must be between 1 and 10000. Values above 10000 are coerced to 10000.
page_token	string A page token, received from a previous ExportTensorboardTimeSeriesData call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to ExportTensorboardTimeSeriesData must match the call that provided the page token.
order_by	string Field to use to sort the TensorboardTimeSeries' data. By default, TensorboardTimeSeries' data is returned in a pseudo random order.

ExportTensorboardTimeSeriesDataResponse

Response message for TensorboardService.ExportTensorboardTimeSeriesData.

Fields
time_series_data_points[]	TimeSeriesDataPoint The returned time series data points.
next_page_token	string A token, which can be sent as page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

Extension

Extensions are tools for large language models to access external data, run computations, etc.

Fields
name	string Identifier. The resource name of the Extension.
display_name	string Required. The display name of the Extension. The name can be up to 128 characters long and can consist of any UTF-8 characters.
description	string Optional. The description of the Extension.
create_time	Timestamp Output only. Timestamp when this Extension was created.
update_time	Timestamp Output only. Timestamp when this Extension was most recently updated.
etag	string Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
manifest	ExtensionManifest Required. Manifest of the Extension.
extension_operations[]	ExtensionOperation Output only. Supported operations.
runtime_config	RuntimeConfig Optional. Runtime config controlling the runtime behavior of this Extension.
tool_use_examples[]	ToolUseExample Optional. Examples to illustrate the usage of the extension as a tool.
private_service_connect_config	ExtensionPrivateServiceConnectConfig Optional. The PrivateServiceConnect config for the extension. If specified, the service endpoints associated with the Extension should be registered with private network access in the provided Service Directory. If the service contains more than one endpoint with a network, the service will arbitrarilty choose one of the endpoints to use for extension execution.
satisfies_pzs	bool Output only. Reserved for future use.
satisfies_pzi	bool Output only. Reserved for future use.

ExtensionManifest

Manifest spec of an Extension needed for runtime execution.

Fields
name	string Required. Extension name shown to the LLM. The name can be up to 128 characters long.
description	string Required. The natural language description shown to the LLM. It should describe the usage of the extension, and is essential for the LLM to perform reasoning. e.g., if the extension is a data store, you can let the LLM know what data it contains.
api_spec	ApiSpec Required. Immutable. The API specification shown to the LLM.
auth_config	AuthConfig Required. Immutable. Type of auth supported by this extension.

ApiSpec

The API specification shown to the LLM.

Fields
Union field api_spec. api_spec can be only one of the following:
open_api_yaml	string The API spec in Open API standard and YAML format.
open_api_gcs_uri	string Cloud Storage URI pointing to the OpenAPI spec.

ExtensionOperation

Operation of an extension.

Fields
operation_id	string Operation ID that uniquely identifies the operations among the extension. See: "Operation Object" in https://swagger.io/specification/. This field is parsed from the OpenAPI spec. For HTTP extensions, if it does not exist in the spec, we will generate one from the HTTP method and path.
function_declaration	FunctionDeclaration Output only. Structured representation of a function declaration as defined by the OpenAPI Spec.

ExtensionPrivateServiceConnectConfig

PrivateExtensionConfig configuration for the extension.

Fields

service_directory

string Required. The Service Directory resource name in which the service endpoints associated to the extension are registered. Format: projects/{project_id}/locations/{location_id}/namespaces/{namespace_id}/services/{service_id} The Agent Platform Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) should be granted servicedirectory.viewer and servicedirectory.pscAuthorizedService roles on the resource.

Fact

The fact used in grounding.

Fields
query	string Query that is used to retrieve this fact.
title	string If present, it refers to the title of this fact.
uri	string If present, this uri links to the source of the fact.
summary	string If present, the summary/snippet of the fact.
vector_distance (deprecated)	double This item is deprecated! If present, the distance between the query vector and this fact vector.
score	double If present, according to the underlying Vector DB and the selected metric type, the score can be either the distance or the similarity between the query and the fact and its range depends on the metric type. For example, if the metric type is COSINE_DISTANCE, it represents the distance between the query and the fact. The larger the distance, the less relevant the fact is to the query. The range is [0, 2], while 0 means the most relevant and 2 means the least relevant.
chunk	RagChunk If present, chunk properties.

FailedRubric

Represents a specific failed rubric and the associated analysis.

Fields
rubric_id	string The unique ID of the rubric (if available from the metric source). Clients use this ID to query the corresponding rubric verdict.
classification_rationale	string The rationale provided by the Loss Analysis Classifier for why this failure maps to this specific Loss Cluster. e.g., "The agent claimed an action without a tool call, matching 'Hallucination of Action'."

FasterDeploymentConfig

Configuration for faster model deployment.

Fields
fast_tryout_enabled	bool If true, enable fast tryout feature for this deployed model.

Feature

Feature Metadata information. For example, color is a feature that describes an apple.

Fields
name	string Immutable. Name of the Feature. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}/features/{feature} projects/{project}/locations/{location}/featureGroups/{feature_group}/features/{feature} The last part feature is assigned by the client. The feature can be up to 64 characters long and can consist only of ASCII Latin letters A-Z and a-z, underscore(_), and ASCII digits 0-9 starting with a letter. The value will be unique given an entity type.
description	string Description of the Feature.
value_type	ValueType Immutable. Only applicable for Agent Platform Feature Store (Legacy). Type of Feature value.
create_time	Timestamp Output only. Only applicable for Agent Platform Feature Store (Legacy). Timestamp when this EntityType was created.
update_time	Timestamp Output only. Only applicable for Agent Platform Feature Store (Legacy). Timestamp when this EntityType was most recently updated.
labels	map<string, string> Optional. The labels with user-defined metadata to organize your Features. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
etag	string Used to perform a consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
monitoring_config (deprecated)	FeaturestoreMonitoringConfig This item is deprecated! Optional. Only applicable for Agent Platform Feature Store (Legacy). Deprecated: The custom monitoring configuration for this Feature, if not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If this is populated with [FeaturestoreMonitoringConfig.disabled][] = true, snapshot analysis monitoring is disabled; if [FeaturestoreMonitoringConfig.monitoring_interval][] specified, snapshot analysis monitoring is enabled. Otherwise, snapshot analysis monitoring config is same as the EntityType's this Feature belongs to.
disable_monitoring	bool Optional. Only applicable for Agent Platform Feature Store (Legacy). If not set, use the monitoring_config defined for the EntityType this Feature belongs to. Only Features with type (Feature.ValueType) BOOL, STRING, DOUBLE or INT64 can enable monitoring. If set to true, all types of data monitoring are disabled despite the config on EntityType.
monitoring_stats[]	FeatureStatsAnomaly Output only. Only applicable for Agent Platform Feature Store (Legacy). A list of historical SnapshotAnalysis stats requested by user, sorted by FeatureStatsAnomaly.start_time descending.
monitoring_stats_anomalies[]	MonitoringStatsAnomaly Output only. Only applicable for Agent Platform Feature Store (Legacy). The list of historical stats and anomalies with specified objectives.
feature_stats_and_anomaly[]	FeatureStatsAndAnomaly Output only. Only applicable for Agent Platform Feature Store. The list of historical stats and anomalies.
version_column_name	string Only applicable for Agent Platform Feature Store. The name of the BigQuery Table/View column hosting data for this version. If no value is provided, will use feature_id.
point_of_contact	string Entity responsible for maintaining this feature. Can be comma separated list of email addresses or URIs.

MonitoringStatsAnomaly

A list of historical SnapshotAnalysis or ImportFeaturesAnalysis stats requested by user, sorted by FeatureStatsAnomaly.start_time descending.

Fields
objective	Objective Output only. The objective for each stats.
feature_stats_anomaly	FeatureStatsAnomaly Output only. The stats and anomalies generated at specific timestamp.

Objective

If the objective in the request is both Import Feature Analysis and Snapshot Analysis, this objective could be one of them. Otherwise, this objective should be the same as the objective in the request.

Enums
OBJECTIVE_UNSPECIFIED	If it's OBJECTIVE_UNSPECIFIED, monitoring_stats will be empty.
IMPORT_FEATURE_ANALYSIS	Stats are generated by Import Feature Analysis.
SNAPSHOT_ANALYSIS	Stats are generated by Snapshot Analysis.

ValueType

Only applicable for Agent Platform Legacy Feature Store. An enum representing the value type of a feature.

Enums
VALUE_TYPE_UNSPECIFIED	The value type is unspecified.
BOOL	Used for Feature that is a boolean.
BOOL_ARRAY	Used for Feature that is a list of boolean.
DOUBLE	Used for Feature that is double.
DOUBLE_ARRAY	Used for Feature that is a list of double.
INT64	Used for Feature that is INT64.
INT64_ARRAY	Used for Feature that is a list of INT64.
STRING	Used for Feature that is string.
STRING_ARRAY	Used for Feature that is a list of String.
BYTES	Used for Feature that is bytes.
STRUCT	Used for Feature that is struct.

FeatureGroup

Agent Platform Feature Group.

Fields
name	string Identifier. Name of the FeatureGroup. Format: projects/{project}/locations/{location}/featureGroups/{featureGroup}
create_time	Timestamp Output only. Timestamp when this FeatureGroup was created.
update_time	Timestamp Output only. Timestamp when this FeatureGroup was last updated.
etag	string Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map<string, string> Optional. The labels with user-defined metadata to organize your FeatureGroup. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureGroup(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
description	string Optional. Description of the FeatureGroup.
service_agent_type	ServiceAgentType Optional. Service agent type used during jobs under a FeatureGroup. By default, the Agent Platform Service Agent is used. When using an IAM Policy to isolate this FeatureGroup within a project, a separate service account should be provisioned by setting this field to SERVICE_AGENT_TYPE_FEATURE_GROUP. This will generate a separate service account to access the BigQuery source table.
service_account_email	string Output only. A Service Account unique to this FeatureGroup. The role bigquery.dataViewer should be granted to this service account to allow Agent Platform Feature Store to access source data while running jobs under this FeatureGroup.
Union field source. source can be only one of the following:
big_query	BigQuery Indicates that features for this group come from BigQuery Table/View. By default treats the source as a sparse time series source. The BigQuery source table or view must have at least one entity ID column and a column named feature_timestamp.

BigQuery

Input source type for BigQuery Tables and Views.

Fields
big_query_source	BigQuerySource Required. Immutable. The BigQuery source URI that points to either a BigQuery Table or View.
entity_id_columns[]	string Optional. Columns to construct entity_id / row keys. If not provided defaults to entity_id.
static_data_source	bool Optional. Set if the data source is not a time-series.
time_series	TimeSeries Optional. If the source is a time-series source, this can be set to control how downstream sources (ex: FeatureView ) will treat time-series sources. If not set, will treat the source as a time-series source with feature_timestamp as timestamp column and no scan boundary.
dense	bool Optional. If set, all feature values will be fetched from a single row per unique entityId including nulls. If not set, will collapse all rows for each unique entityId into a singe row with any non-null values if present, if no non-null values are present will sync null. ex: If source has schema (entity_id, feature_timestamp, f0, f1) and the following rows: (e1, 2020-01-01T10:00:00.123Z, 10, 15) (e1, 2020-02-01T10:00:00.123Z, 20, null) If dense is set, (e1, 20, null) is synced to online stores. If dense is not set, (e1, 20, 15) is synced to online stores.

TimeSeries

Fields
timestamp_column	string Optional. Column hosting timestamp values for a time-series source. Will be used to determine the latest feature_values for each entity. Optional. If not provided, column named feature_timestamp of type TIMESTAMP will be used.

ServiceAgentType

Service agent type used during jobs under a FeatureGroup.

Enums
SERVICE_AGENT_TYPE_UNSPECIFIED	By default, the project-level Agent Platform Service Agent is enabled.
SERVICE_AGENT_TYPE_PROJECT	Specifies the project-level Agent Platform Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).
SERVICE_AGENT_TYPE_FEATURE_GROUP	Enable a FeatureGroup service account to be created by Agent Platform and output in the field service_account_email. This service account will be used to read from the source BigQuery table during jobs under a FeatureGroup.

FeatureMonitor

Agent Platform Feature Monitor.

Fields
name	string Identifier. Name of the FeatureMonitor. Format: projects/{project}/locations/{location}/featureGroups/{featureGroup}/featureMonitors/{featureMonitor}
create_time	Timestamp Output only. Timestamp when this FeatureMonitor was created.
update_time	Timestamp Output only. Timestamp when this FeatureMonitor was last updated.
etag	string Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map<string, string> Optional. The labels with user-defined metadata to organize your FeatureMonitor. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureMonitor(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
description	string Optional. Description of the FeatureMonitor.
schedule_config	ScheduleConfig Required. Schedule config for the FeatureMonitor.
feature_selection_config	FeatureSelectionConfig Required. Feature selection config for the FeatureMonitor.

FeatureMonitorJob

Agent Platform Feature Monitor Job.

Fields
name	string Identifier. Name of the FeatureMonitorJob. Format: projects/{project}/locations/{location}/featureGroups/{feature_group}/featureMonitors/{feature_monitor}/featureMonitorJobs/{feature_monitor_job}.
create_time	Timestamp Output only. Timestamp when this FeatureMonitorJob was created. Creation of a FeatureMonitorJob means that the job is pending / waiting for sufficient resources but may not have started running yet.
final_status	Status Output only. Final status of the FeatureMonitorJob.
job_summary	JobSummary Output only. Summary from the FeatureMonitorJob.
labels	map<string, string> Optional. The labels with user-defined metadata to organize your FeatureMonitorJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureMonitor(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
description	string Optional. Description of the FeatureMonitor.
drift_base_feature_monitor_job_id	int64 Output only. FeatureMonitorJob ID comparing to which the drift is calculated.
drift_base_snapshot_time	Timestamp Output only. Data snapshot time comparing to which the drift is calculated.
feature_selection_config	FeatureSelectionConfig Output only. Feature selection config used when creating FeatureMonitorJob.
trigger_type	FeatureMonitorJobTrigger Output only. Trigger type of the Feature Monitor Job.

FeatureMonitorJobTrigger

Choices of the trigger type.

Enums
FEATURE_MONITOR_JOB_TRIGGER_UNSPECIFIED	Trigger type unspecified.
FEATURE_MONITOR_JOB_TRIGGER_PERIODIC	Triggered by periodic schedule.
FEATURE_MONITOR_JOB_TRIGGER_ON_DEMAND	Triggered on demand by CreateFeatureMonitorJob request.

JobSummary

Summary from the FeatureMonitorJob.

Fields
total_slot_ms	int64 Output only. BigQuery slot milliseconds consumed.
feature_stats_and_anomalies[]	FeatureStatsAndAnomaly Output only. Features and their stats and anomalies

FeatureNoiseSigma

Noise sigma by features. Noise sigma represents the standard deviation of the gaussian kernel that will be used to add noise to interpolated inputs prior to computing gradients.

Fields
noise_sigma[]	NoiseSigmaForFeature Noise sigma per feature. No noise is added to features that are not set.

NoiseSigmaForFeature

Noise sigma for a single feature.

Fields
name	string The name of the input feature for which noise sigma is provided. The features are defined in explanation metadata inputs.
sigma	float This represents the standard deviation of the Gaussian kernel that will be used to add noise to the feature prior to computing gradients. Similar to noise_sigma but represents the noise added to the current feature. Defaults to 0.1.

FeatureOnlineStore

Agent Platform Feature Online Store provides a centralized repository for serving ML features and embedding indexes at low latency. The Feature Online Store is a top-level container.

Fields
name	string Identifier. Name of the FeatureOnlineStore. Format: projects/{project}/locations/{location}/featureOnlineStores/{featureOnlineStore}
create_time	Timestamp Output only. Timestamp when this FeatureOnlineStore was created.
update_time	Timestamp Output only. Timestamp when this FeatureOnlineStore was last updated.
etag	string Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map<string, string> Optional. The labels with user-defined metadata to organize your FeatureOnlineStore. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
state	State Output only. State of the featureOnlineStore.
dedicated_serving_endpoint	DedicatedServingEndpoint Optional. The dedicated serving endpoint for this FeatureOnlineStore, which is different from common Vertex service endpoint.
embedding_management (deprecated)	EmbeddingManagement This item is deprecated! Optional. Deprecated: This field is no longer needed anymore and embedding management is automatically enabled when specifying Optimized storage type.
encryption_spec	EncryptionSpec Optional. Customer-managed encryption key spec for data storage. If set, online store will be secured by this key.
satisfies_pzs	bool Output only. Reserved for future use.
satisfies_pzi	bool Output only. Reserved for future use.
Union field storage_type. storage_type can be only one of the following:
bigtable	Bigtable Contains settings for the Cloud Bigtable instance that will be created to serve featureValues for all FeatureViews under this FeatureOnlineStore.
optimized	Optimized Contains settings for the Optimized store that will be created to serve featureValues for all FeatureViews under this FeatureOnlineStore. When choose Optimized storage type, need to set PrivateServiceConnectConfig.enable_private_service_connect to use private endpoint. Otherwise will use public endpoint by default.

Bigtable

Fields
auto_scaling	AutoScaling Required. Autoscaling config applied to Bigtable Instance.
enable_direct_bigtable_access	bool Optional. It true, enable direct access to the Bigtable instance.
bigtable_metadata	BigtableMetadata Output only. Metadata of the Bigtable instance. Output only.
zone	string Optional. The zone where the underlying Bigtable cluster for the primary Bigtable instance will be provisioned. Only the zone must be provided. For example, only "us-central1-a" should be provided.

AutoScaling

Fields
min_node_count	int32 Required. The minimum number of nodes to scale down to. Must be greater than or equal to 1.
max_node_count	int32 Required. The maximum number of nodes to scale up to. Must be greater than or equal to min_node_count, and less than or equal to 10 times of 'min_node_count'.
cpu_utilization_target	int32 Optional. A percentage of the cluster's CPU capacity. Can be from 10% to 80%. When a cluster's CPU utilization exceeds the target that you have set, Bigtable immediately adds nodes to the cluster. When CPU utilization is substantially lower than the target, Bigtable removes nodes. If not set will default to 50%.

BigtableMetadata

Metadata of the Bigtable instance. This is used by direct read access to the Bigtable in tenant project.

Fields
tenant_project_id	string Tenant project ID.
instance_id	string The Cloud Bigtable instance id.
table_id	string The Cloud Bigtable table id.

DedicatedServingEndpoint

The dedicated serving endpoint for this FeatureOnlineStore. Only need to set when you choose Optimized storage type. Public endpoint is provisioned by default.

Fields
public_endpoint_domain_name	string Output only. This field will be populated with the domain name to use for this FeatureOnlineStore
private_service_connect_config	PrivateServiceConnectConfig Optional. Private service connect config. The private service connection is available only for Optimized storage type, not for embedding management now. If PrivateServiceConnectConfig.enable_private_service_connect set to true, customers will use private service connection to send request. Otherwise, the connection will set to public endpoint.
service_attachment	string Output only. The name of the service attachment resource. Populated if private service connect is enabled and after FeatureViewSync is created.

EmbeddingManagement

This item is deprecated!

Deprecated: This sub message is no longer needed anymore and embedding management is automatically enabled when specifying Optimized storage type. Contains settings for embedding management.

Fields
enabled	bool Optional. Immutable. Whether to enable embedding management in this FeatureOnlineStore. It's immutable after creation to ensure the FeatureOnlineStore availability.

Optimized

This type has no fields.

Optimized storage type

State

Possible states a featureOnlineStore can have.

Enums
STATE_UNSPECIFIED	Default value. This value is unused.
STABLE	State when the featureOnlineStore configuration is not being updated and the fields reflect the current configuration of the featureOnlineStore. The featureOnlineStore is usable in this state.
UPDATING	The state of the featureOnlineStore configuration when it is being updated. During an update, the fields reflect either the original configuration or the updated configuration of the featureOnlineStore. The featureOnlineStore is still usable in this state.

FeatureSelectionConfig

Feature selection configuration for the FeatureMonitor.

Fields
feature_configs[]	FeatureConfig Optional. A list of features to be monitored and each feature's drift threshold.

FeatureConfig

Feature configuration.

Fields
feature_id	string Required. The ID of the feature resource. Final component of the Feature's resource name.
drift_threshold	double Optional. Drift threshold. If calculated difference with baseline data larger than threshold, it will be considered as the feature has drift. If not present, the threshold will be default to 0.3. Must be in range [0, 1).

FeatureSelector

Selector for Features of an EntityType.

Fields
id_matcher	IdMatcher Required. Matches Features based on ID.

FeatureStatsAndAnomaly

Stats and Anomaly generated by FeatureMonitorJobs. Anomaly only includes Drift.

Fields
feature_id	string Feature Id.
feature_stats	Value Feature stats. e.g. histogram buckets. In the format of tensorflow.metadata.v0.DatasetFeatureStatistics.
distribution_deviation	double Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
drift_detection_threshold	double This is the threshold used when detecting drifts, which is set in FeatureMonitor.FeatureSelectionConfig.FeatureConfig.drift_threshold
drift_detected	bool If set to true, indicates current stats is detected as and comparing with baseline stats.
stats_time	Timestamp The timestamp we take snapshot for feature values to generate stats.
feature_monitor_job_id	int64 The ID of the FeatureMonitorJob that generated this FeatureStatsAndAnomaly.
feature_monitor_id	string The ID of the FeatureMonitor that this FeatureStatsAndAnomaly generated according to.

FeatureStatsAndAnomalySpec

Defines how to select FeatureStatsAndAnomaly to be populated in response. If set, retrieves FeatureStatsAndAnomaly generated by FeatureMonitors based on this spec.

Fields
stats_time_range	Interval Optional. If set, return all stats generated between [start_time, end_time). If latest_stats_count is set, return the most recent count of stats within the stats_time_range.
latest_stats_count	int32 Optional. If set, returns the most recent count of stats. Valid value is [0, 100]. If stats_time_range is set, return most recent count of stats within the stats_time_range.

FeatureStatsAnomaly

Stats and Anomaly generated at specific timestamp for specific Feature. The start_time and end_time are used to define the time range of the dataset that current stats belongs to, e.g. prediction traffic is bucketed into prediction datasets by time window. If the Dataset is not defined by time window, start_time = end_time. Timestamp of the stats and anomalies always refers to end_time. Raw stats and anomalies are stored in stats_uri or anomaly_uri in the tensorflow defined protos. Field data_stats contains almost identical information with the raw stats in Agent Platform defined proto, for UI to display.

Fields
score	double Feature importance score, only populated when cross-feature monitoring is enabled. For now only used to represent feature attribution score within range [0, 1] for ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_SKEW and ModelDeploymentMonitoringObjectiveType.FEATURE_ATTRIBUTION_DRIFT.
stats_uri	string Path of the stats file for current feature values in Cloud Storage bucket. Format: gs:////stats. Example: gs://monitoring_bucket/feature_name/stats. Stats are stored as binary format with Protobuf message tensorflow.metadata.v0.FeatureNameStatistics.
anomaly_uri	string Path of the anomaly file for current feature values in Cloud Storage bucket. Format: gs:////anomalies. Example: gs://monitoring_bucket/feature_name/anomalies. Stats are stored as binary format with Protobuf message Anoamlies are stored as binary format with Protobuf message tensorflow.metadata.v0.AnomalyInfo.
distribution_deviation	double Deviation from the current stats to baseline stats. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence.
anomaly_detection_threshold	double This is the threshold used when detecting anomalies. The threshold can be changed by user, so this one might be different from ThresholdConfig.value.
start_time	Timestamp The start timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), start_time is only used to indicate the monitoring intervals, so it always equals to (end_time - monitoring_interval).
end_time	Timestamp The end timestamp of window where stats were generated. For objectives where time window doesn't make sense (e.g. Featurestore Snapshot Monitoring), end_time indicates the timestamp of the data used to generate stats (e.g. timestamp we take snapshots for feature values).

FeatureValue

Value for a feature.

Fields
metadata	Metadata Metadata of feature value.
Union field value. Value for the feature. value can be only one of the following:
bool_value	bool Bool type feature value.
double_value	double Double type feature value.
int64_value	int64 Int64 feature value.
string_value	string String feature value.
bool_array_value	BoolArray A list of bool type feature value.
double_array_value	DoubleArray A list of double type feature value.
int64_array_value	Int64Array A list of int64 type feature value.
string_array_value	StringArray A list of string type feature value.
bytes_value	bytes Bytes feature value.
struct_value	StructValue A struct type feature value.

Metadata

Metadata of feature value.

Fields
generate_time	Timestamp Feature generation timestamp. Typically, it is provided by user at feature ingestion time. If not, feature store will use the system timestamp when the data is ingested into feature store. Legacy Feature Store: For streaming ingestion, the time, aligned by days, must be no older than five years (1825 days) and no later than one year (366 days) in the future.

FeatureValueDestination

A destination location for Feature values and format.

Fields
Union field destination. destination can be only one of the following:
bigquery_destination	BigQueryDestination Output in BigQuery format. BigQueryDestination.output_uri in FeatureValueDestination.bigquery_destination must refer to a table.
tfrecord_destination	TFRecordDestination Output in TFRecord format. Below are the mapping from Feature value type in Featurestore to Feature value type in TFRecord: Value type in Featurestore | Value type in TFRecord DOUBLE, DOUBLE_ARRAY | FLOAT_LIST INT64, INT64_ARRAY | INT64_LIST STRING, STRING_ARRAY, BYTES | BYTES_LIST true -> byte_string("true"), false -> byte_string("false") BOOL, BOOL_ARRAY (true, false) | BYTES_LIST
csv_destination	CsvDestination Output in CSV format. Array Feature value types are not allowed in CSV format.

FeatureValueList

Container for list of values.

Fields
values[]	FeatureValue A list of feature values. All of them should be the same data type.

FeatureView

FeatureView is representation of values that the FeatureOnlineStore will serve based on its syncConfig.

Fields
name	string Identifier. Name of the FeatureView. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}
create_time	Timestamp Output only. Timestamp when this FeatureView was created.
update_time	Timestamp Output only. Timestamp when this FeatureView was last updated.
etag	string Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map<string, string> Optional. The labels with user-defined metadata to organize your FeatureViews. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one FeatureOnlineStore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
sync_config	SyncConfig Configures when data is to be synced/updated for this FeatureView. At the end of the sync the latest featureValues for each entityId of this FeatureView are made ready for online serving.
vector_search_config (deprecated)	VectorSearchConfig This item is deprecated! Optional. Deprecated: please use FeatureView.index_config instead.
index_config	IndexConfig Optional. Configuration for index preparation for vector search. It contains the required configurations to create an index from source data, so that approximate nearest neighbor (a.k.a ANN) algorithms search can be performed during online serving.
optimized_config	OptimizedConfig Optional. Configuration for FeatureView created under Optimized FeatureOnlineStore.
service_agent_type	ServiceAgentType Optional. Service agent type used during data sync. By default, the Agent Platform Service Agent is used. When using an IAM Policy to isolate this FeatureView within a project, a separate service account should be provisioned by setting this field to SERVICE_AGENT_TYPE_FEATURE_VIEW. This will generate a separate service account to access the BigQuery source table.
service_account_email	string Output only. A Service Account unique to this FeatureView. The role bigquery.dataViewer should be granted to this service account to allow Agent Platform Feature Store to sync data to the online store.
satisfies_pzs	bool Output only. Reserved for future use.
satisfies_pzi	bool Output only. Reserved for future use.
bigtable_metadata	BigtableMetadata Output only. Metadata containing information about the Cloud Bigtable.
Union field source. source can be only one of the following:
big_query_source	BigQuerySource Optional. Configures how data is supposed to be extracted from a BigQuery source to be loaded onto the FeatureOnlineStore.
feature_registry_source	FeatureRegistrySource Optional. Configures the features from a Feature Registry source that need to be loaded onto the FeatureOnlineStore.
vertex_rag_source	VertexRagSource Optional. The Vertex RAG Source that the FeatureView is linked to.

BigQuerySource

Fields
uri	string Required. The BigQuery view URI that will be materialized on each sync trigger based on FeatureView.SyncConfig.
entity_id_columns[]	string Required. Columns to construct entity_id / row keys.

BigtableMetadata

Metadata for the Cloud Bigtable that supports directly interacting Bigtable instances.

Fields
read_app_profile	string Output only. The Bigtable App Profile to use for reading from Bigtable.

FeatureRegistrySource

A Feature Registry source for features that need to be synced to Online Store.

Fields
feature_groups[]	FeatureGroup Required. List of features that need to be synced to Online Store.
project_number	int64 Optional. The project number of the parent project of the Feature Groups.

FeatureGroup

Features belonging to a single feature group that will be synced to Online Store.

Fields
feature_group_id	string Required. Identifier of the feature group.
feature_ids[]	string Required. Identifiers of features under the feature group.

IndexConfig

Configuration for vector indexing.

Fields
embedding_column	string Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
filter_columns[]	string Optional. Columns of features that're used to filter vector search results.
crowding_column	string Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by FeatureOnlineStoreService.SearchNearestEntities to diversify search results. If NearestNeighborQuery.per_crowding_attribute_neighbor_count is set to K in SearchNearestEntitiesRequest, it's guaranteed that no more than K entities of the same crowding attribute are returned in the response.
distance_measure_type	DistanceMeasureType Optional. The distance measure used in nearest neighbor search.
Union field algorithm_config. The configuration with regard to the algorithms used for efficient search. algorithm_config can be only one of the following:
tree_ah_config	TreeAHConfig Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396
brute_force_config	BruteForceConfig Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
embedding_dimension	int32 Optional. The number of dimensions of the input embedding.

BruteForceConfig

This type has no fields.

Configuration options for using brute force search.

DistanceMeasureType

The distance measure used in nearest neighbor search.

Enums
DISTANCE_MEASURE_TYPE_UNSPECIFIED	Should not be set.
SQUARED_L2_DISTANCE	Euclidean (L_2) Distance.
COSINE_DISTANCE	Cosine Distance. Defined as 1 - cosine similarity. We strongly suggest using DOT_PRODUCT_DISTANCE + UNIT_L2_NORM instead of COSINE distance. Our algorithms have been more optimized for DOT_PRODUCT distance which, when combined with UNIT_L2_NORM, is mathematically equivalent to COSINE distance and results in the same ranking.
DOT_PRODUCT_DISTANCE	Dot Product Distance. Defined as a negative of the dot product.

TreeAHConfig

Configuration options for the tree-AH algorithm.

Fields
leaf_node_embedding_count	int64 Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.

OptimizedConfig

Configuration for FeatureViews created in Optimized FeatureOnlineStore.

Fields
automatic_resources	AutomaticResources Optional. A description of resources that the FeatureView uses, which to large degree are decided by Agent Platform, and optionally allows only a modest additional configuration. If min_replica_count is not set, the default value is 2. If max_replica_count is not set, the default value is 6. The max allowed replica count is 1000.

ServiceAgentType

Service agent type used during data sync.

Enums
SERVICE_AGENT_TYPE_UNSPECIFIED	By default, the project-level Agent Platform Service Agent is enabled.
SERVICE_AGENT_TYPE_PROJECT	Indicates the project-level Agent Platform Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) will be used during sync jobs.
SERVICE_AGENT_TYPE_FEATURE_VIEW	Enable a FeatureView service account to be created by Agent Platform and output in the field service_account_email. This service account will be used to read from the source BigQuery table during sync.

SyncConfig

Configuration for Sync. Only one option is set.

Fields
cron	string Cron schedule (https://en.wikipedia.org/wiki/Cron) to launch scheduled runs. To explicitly set a timezone to the cron tab, apply a prefix in the cron tab: "CRON_TZ=${IANA_TIME_ZONE}" or "TZ=${IANA_TIME_ZONE}". The ${IANA_TIME_ZONE} may only be a valid string from IANA time zone database. For example, "CRON_TZ=America/New_York 1 * * * *", or "TZ=America/New_York 1 * * * *".
continuous	bool Optional. If true, syncs the FeatureView in a continuous manner to Online Store.

VectorSearchConfig

This item is deprecated!

Deprecated. Use IndexConfig instead.

Fields
embedding_column	string Optional. Column of embedding. This column contains the source data to create index for vector search. embedding_column must be set when using vector search.
filter_columns[]	string Optional. Columns of features that're used to filter vector search results.
crowding_column	string Optional. Column of crowding. This column contains crowding attribute which is a constraint on a neighbor list produced by FeatureOnlineStoreService.SearchNearestEntities to diversify search results. If NearestNeighborQuery.per_crowding_attribute_neighbor_count is set to K in SearchNearestEntitiesRequest, it's guaranteed that no more than K entities of the same crowding attribute are returned in the response.
distance_measure_type	DistanceMeasureType Optional. The distance measure used in nearest neighbor search.
Union field algorithm_config. The configuration with regard to the algorithms used for efficient search. algorithm_config can be only one of the following:
tree_ah_config	TreeAHConfig Optional. Configuration options for the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396
brute_force_config	BruteForceConfig Optional. Configuration options for using brute force search, which simply implements the standard linear search in the database for each query. It is primarily meant for benchmarking and to generate the ground truth for approximate search.
embedding_dimension	int32 Optional. The number of dimensions of the input embedding.

BruteForceConfig

This type has no fields.

DistanceMeasureType

Enums
DISTANCE_MEASURE_TYPE_UNSPECIFIED	Should not be set.
SQUARED_L2_DISTANCE	Euclidean (L_2) Distance.
COSINE_DISTANCE	Cosine Distance. Defined as 1 - cosine similarity. We strongly suggest using DOT_PRODUCT_DISTANCE + UNIT_L2_NORM instead of COSINE distance. Our algorithms have been more optimized for DOT_PRODUCT distance which, when combined with UNIT_L2_NORM, is mathematically equivalent to COSINE distance and results in the same ranking.
DOT_PRODUCT_DISTANCE	Dot Product Distance. Defined as a negative of the dot product.

TreeAHConfig

Fields
leaf_node_embedding_count	int64 Optional. Number of embeddings on each leaf node. The default value is 1000 if not set.

VertexRagSource

A Vertex Rag source for features that need to be synced to Online Store.

Fields
uri	string Required. The BigQuery view/table URI that will be materialized on each manual sync trigger. The table/view is expected to have the following columns and types at least: - corpus_id (STRING, NULLABLE/REQUIRED) - file_id (STRING, NULLABLE/REQUIRED) - chunk_id (STRING, NULLABLE/REQUIRED) - chunk_data_type (STRING, NULLABLE/REQUIRED) - chunk_data (STRING, NULLABLE/REQUIRED) - embeddings (FLOAT, REPEATED) - file_original_uri (STRING, NULLABLE/REQUIRED)
rag_corpus_id	int64 Optional. The RAG corpus id corresponding to this FeatureView.

FeatureViewDataFormat

Format of the data in the Feature View.

Enums
FEATURE_VIEW_DATA_FORMAT_UNSPECIFIED	Not set. Will be treated as the KeyValue format.
KEY_VALUE	Return response data in key-value format.
PROTO_STRUCT	Return response data in proto Struct format.

FeatureViewDataKey

Lookup key for a feature view.

Fields
Union field key_oneof. key_oneof can be only one of the following:
key	string String key to use for lookup.
composite_key	CompositeKey The actual Entity ID will be composed from this struct. This should match with the way ID is defined in the FeatureView spec.

CompositeKey

ID that is comprised from several parts (columns).

Fields
parts[]	string Parts to construct Entity ID. Should match with the same ID columns as defined in FeatureView in the same order.

FeatureViewDirectWriteRequest

Request message for FeatureOnlineStoreService.FeatureViewDirectWrite.

Fields
feature_view	string FeatureView resource format projects/{project}/locations/{location}/featureOnlineStores/{featureOnlineStore}/featureViews/{featureView}
data_key_and_feature_values[]	DataKeyAndFeatureValues Required. The data keys and associated feature values.

DataKeyAndFeatureValues

A data key and associated feature values to write to the feature view.

Fields
data_key	FeatureViewDataKey The data key.
features[]	Feature List of features to write.

Feature

Feature name & value pair.

Fields
name	string Feature short name.
Union field data_oneof. Feature value data to write. data_oneof can be only one of the following:
value	FeatureValue Feature value. A user provided timestamp may be set in the FeatureValue.metadata.generate_time field.
value_and_timestamp	FeatureValueAndTimestamp Feature value and timestamp.

FeatureValueAndTimestamp

Feature value and timestamp.

Fields
value	FeatureValue The feature value.
timestamp	Timestamp The feature timestamp to store with this value. If not set, then the Feature Store server will generate a timestamp when it receives the write request.

FeatureViewDirectWriteResponse

Response message for FeatureOnlineStoreService.FeatureViewDirectWrite.

Fields
status	Status Response status for the keys listed in FeatureViewDirectWriteResponse.write_responses. The error only applies to the listed data keys - the stream will remain open for further [FeatureOnlineStoreService.FeatureViewDirectWriteRequest][] requests. Partial failures (e.g. if the first 10 keys of a request fail, but the rest succeed) from a single request may result in multiple responses - there will be one response for the successful request keys and one response for the failing request keys.
write_responses[]	WriteResponse Details about write for each key. If status is not OK, WriteResponse.data_key will have the key with error, but WriteResponse.online_store_write_time will not be present.

WriteResponse

Details about the write for each key.

Fields
data_key	FeatureViewDataKey What key is this write response associated with.
online_store_write_time	Timestamp When the feature values were written to the online store. If FeatureViewDirectWriteResponse.status is not OK, this field is not populated.

FeatureViewSync

FeatureViewSync is a representation of sync operation which copies data from data source to Feature View in Online Store.

Fields
name	string Identifier. Name of the FeatureViewSync. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}/featureViewSyncs/{feature_view_sync}
create_time	Timestamp Output only. Time when this FeatureViewSync is created. Creation of a FeatureViewSync means that the job is pending / waiting for sufficient resources but may not have started the actual data transfer yet.
run_time	Interval Output only. Time when this FeatureViewSync is finished.
final_status	Status Output only. Final status of the FeatureViewSync.
sync_summary	SyncSummary Output only. Summary of the sync job.
satisfies_pzs	bool Output only. Reserved for future use.
satisfies_pzi	bool Output only. Reserved for future use.

SyncSummary

Summary from the Sync job. For continuous syncs, the summary is updated periodically. For batch syncs, it gets updated on completion of the sync.

Fields
row_synced	int64 Output only. Total number of rows synced.
total_slot	int64 Output only. BigQuery slot milliseconds consumed for the sync job.
system_watermark_time	Timestamp Lower bound of the system time watermark for the sync job. This is only set for continuously syncing feature views.

Featurestore

Agent Platform Feature Store provides a centralized repository for organizing, storing, and serving ML features. The Featurestore is a top-level container for your features and their values.

Fields
name	string Output only. Name of the Featurestore. Format: projects/{project}/locations/{location}/featurestores/{featurestore}
create_time	Timestamp Output only. Timestamp when this Featurestore was created.
update_time	Timestamp Output only. Timestamp when this Featurestore was last updated.
etag	string Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map<string, string> Optional. The labels with user-defined metadata to organize your Featurestore. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Featurestore(System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
online_serving_config	OnlineServingConfig Optional. Config for online storage resources. The field should not co-exist with the field of OnlineStoreReplicationConfig. If both of it and OnlineStoreReplicationConfig are unset, the feature store will not have an online store and cannot be used for online serving.
state	State Output only. State of the featurestore.
online_storage_ttl_days	int32 Optional. TTL in days for feature values that will be stored in online serving storage. The Feature Store online storage periodically removes obsolete feature values older than online_storage_ttl_days since the feature generation time. Note that online_storage_ttl_days should be less than or equal to offline_storage_ttl_days for each EntityType under a featurestore. If not set, default to 4000 days
encryption_spec	EncryptionSpec Optional. Customer-managed encryption key spec for data storage. If set, both of the online and offline data storage will be secured by this key.
satisfies_pzs	bool Output only. Reserved for future use.
satisfies_pzi	bool Output only. Reserved for future use.

OnlineServingConfig

OnlineServingConfig specifies the details for provisioning online serving resources.

Fields
fixed_node_count	int32 The number of nodes for the online store. The number of nodes doesn't scale automatically, but you can manually update the number of nodes. If set to 0, the featurestore will not have an online store and cannot be used for online serving.
scaling	Scaling Online serving scaling configuration. Only one of fixed_node_count and scaling can be set. Setting one will reset the other.

Scaling

Online serving scaling configuration. If min_node_count and max_node_count are set to the same value, the cluster will be configured with the fixed number of node (no auto-scaling).

Fields
min_node_count	int32 Required. The minimum number of nodes to scale down to. Must be greater than or equal to 1.
max_node_count	int32 The maximum number of nodes to scale up to. Must be greater than min_node_count, and less than or equal to 10 times of 'min_node_count'.
cpu_utilization_target	int32 Optional. The cpu utilization that the Autoscaler should be trying to achieve. This number is on a scale from 0 (no utilization) to 100 (total utilization), and is limited between 10 and 80. When a cluster's CPU utilization exceeds the target that you have set, Bigtable immediately adds nodes to the cluster. When CPU utilization is substantially lower than the target, Bigtable removes nodes. If not set or set to 0, default to 50.

State

Possible states a featurestore can have.

Enums
STATE_UNSPECIFIED	Default value. This value is unused.
STABLE	State when the featurestore configuration is not being updated and the fields reflect the current configuration of the featurestore. The featurestore is usable in this state.
UPDATING	The state of the featurestore configuration when it is being updated. During an update, the fields reflect either the original configuration or the updated configuration of the featurestore. For example, online_serving_config.fixed_node_count can take minutes to update. While the update is in progress, the featurestore is in the UPDATING state, and the value of fixed_node_count can be the original value or the updated value, depending on the progress of the operation. Until the update completes, the actual number of nodes can still be the original value of fixed_node_count. The featurestore is still usable in this state.

FeaturestoreMonitoringConfig

Configuration of how features in Featurestore are monitored.

Fields
snapshot_analysis	SnapshotAnalysis The config for Snapshot Analysis Based Feature Monitoring.
import_features_analysis	ImportFeaturesAnalysis The config for ImportFeatures Analysis Based Feature Monitoring.
numerical_threshold_config	ThresholdConfig Threshold for numerical features of anomaly detection. This is shared by all objectives of Featurestore Monitoring for numerical features (i.e. Features with type (Feature.ValueType) DOUBLE or INT64).
categorical_threshold_config	ThresholdConfig Threshold for categorical features of anomaly detection. This is shared by all types of Featurestore Monitoring for categorical features (i.e. Features with type (Feature.ValueType) BOOL or STRING).

ImportFeaturesAnalysis

Configuration of the Featurestore's ImportFeature Analysis Based Monitoring. This type of analysis generates statistics for values of each Feature imported by every ImportFeatureValues operation.

Fields
state	State Whether to enable / disable / inherite default hebavior for import features analysis.
anomaly_detection_baseline	Baseline The baseline used to do anomaly detection for the statistics generated by import features analysis.

Baseline

Defines the baseline to do anomaly detection for feature values imported by each ImportFeatureValues operation.

Enums
BASELINE_UNSPECIFIED	Should not be used.
LATEST_STATS	Choose the later one statistics generated by either most recent snapshot analysis or previous import features analysis. If non of them exists, skip anomaly detection and only generate a statistics.
MOST_RECENT_SNAPSHOT_STATS	Use the statistics generated by the most recent snapshot analysis if exists.
PREVIOUS_IMPORT_FEATURES_STATS	Use the statistics generated by the previous import features analysis if exists.

State

The state defines whether to enable ImportFeature analysis.

Enums
STATE_UNSPECIFIED	Should not be used.
DEFAULT	The default behavior of whether to enable the monitoring. EntityType-level config: disabled. Feature-level config: inherited from the configuration of EntityType this Feature belongs to.
ENABLED	Explicitly enables import features analysis. EntityType-level config: by default enables import features analysis for all Features under it. Feature-level config: enables import features analysis regardless of the EntityType-level config.
DISABLED	Explicitly disables import features analysis. EntityType-level config: by default disables import features analysis for all Features under it. Feature-level config: disables import features analysis regardless of the EntityType-level config.

SnapshotAnalysis

Configuration of the Featurestore's Snapshot Analysis Based Monitoring. This type of analysis generates statistics for each Feature based on a snapshot of the latest feature value of each entities every monitoring_interval.

Fields
disabled	bool The monitoring schedule for snapshot analysis. For EntityType-level config: unset / disabled = true indicates disabled by default for Features under it; otherwise by default enable snapshot analysis monitoring with monitoring_interval for Features under it. Feature-level config: disabled = true indicates disabled regardless of the EntityType-level config; unset monitoring_interval indicates going with EntityType-level config; otherwise run snapshot analysis monitoring with monitoring_interval regardless of the EntityType-level config. Explicitly Disable the snapshot analysis based monitoring.
monitoring_interval (deprecated)	Duration This item is deprecated! Configuration of the snapshot analysis based monitoring pipeline running interval. The value is rolled up to full day. If both monitoring_interval_days and the deprecated monitoring_interval field are set when creating/updating EntityTypes/Features, monitoring_interval_days will be used.
monitoring_interval_days	int32 Configuration of the snapshot analysis based monitoring pipeline running interval. The value indicates number of days.
staleness_days	int32 Customized export features time window for snapshot analysis. Unit is one day. Default value is 3 weeks. Minimum value is 1 day. Maximum value is 4000 days.

ThresholdConfig

The config for Featurestore Monitoring threshold.

Fields
Union field threshold. threshold can be only one of the following:
value	double Specify a threshold value that can trigger the alert. 1. For categorical feature, the distribution distance is calculated by L-inifinity norm. 2. For numerical feature, the distribution distance is calculated by Jensen–Shannon divergence. Each feature must have a non-zero threshold if they need to be monitored. Otherwise no alert will be triggered for that feature.

FetchExamplesRequest

Request message for ExampleStoreService.FetchExamples.

Fields
example_store	string Required. The name of the ExampleStore resource that the examples should be fetched from. Format: projects/{project}/locations/{location}/exampleStores/{example_store}
page_size	int32 Optional. The maximum number of examples to return. The service may return fewer than this value. If unspecified, at most 100 examples will be returned.
page_token	string Optional. The next_page_token value returned from a previous list [ExampleStoreService.FetchExamplesResponse][] call.
example_ids[]	string Optional. Example IDs to fetch. If both metadata filters and Example IDs are specified, then both ID and metadata filtering will be applied.
Union field metadata_filter. The example type-specific filters to be applied to the fetch operation. metadata_filter can be only one of the following:
stored_contents_example_filter	StoredContentsExampleFilter The metadata filters for StoredContentsExamples.

FetchExamplesResponse

Response message for ExampleStoreService.FetchExamples.

Fields
examples[]	Example The examples in the Example Store that satisfy the metadata filters.
next_page_token	string A token, which can be sent as FetchExamplesRequest.page_token to retrieve the next page. Absence of this field indicates there are no subsequent pages.

FetchFeatureValuesRequest

Request message for FeatureOnlineStoreService.FetchFeatureValues. All the features under the requested feature view will be returned.

Fields
feature_view	string Required. FeatureView resource format projects/{project}/locations/{location}/featureOnlineStores/{featureOnlineStore}/featureViews/{featureView}
data_key	FeatureViewDataKey Optional. The request key to fetch feature values for.
data_format	FeatureViewDataFormat Optional. Response data format. If not set, FeatureViewDataFormat.KEY_VALUE will be used.
format (deprecated)	Format This item is deprecated! Specify response data format. If not set, KeyValue format will be used. Deprecated. Use FetchFeatureValuesRequest.data_format.
Union field entity_id. Entity ID to fetch feature values for. Deprecated. Use FetchFeatureValuesRequest.data_key. entity_id can be only one of the following:
id (deprecated)	string This item is deprecated! Simple ID. The whole string will be used as is to identify Entity to fetch feature values for.

Format

This item is deprecated!

Format of the response data.

Enums
FORMAT_UNSPECIFIED	Not set. Will be treated as the KeyValue format.
KEY_VALUE	Return response data in key-value format.
PROTO_STRUCT	Return response data in proto Struct format.

FetchFeatureValuesResponse

Response message for FeatureOnlineStoreService.FetchFeatureValues

Fields
data_key	FeatureViewDataKey The data key associated with this response. Will only be populated for FeatureOnlineStoreService.StreamingFetchFeatureValues RPCs.
Union field format. format can be only one of the following:
key_values	FeatureNameValuePairList Feature values in KeyValue format.
proto_struct	Struct Feature values in proto Struct format.

FeatureNameValuePairList

Response structure in the format of key (feature name) and (feature) value pair.

Fields
features[]	FeatureNameValuePair List of feature names and values.

FeatureNameValuePair

Feature name & value pair.

Fields
name	string Feature short name.
Union field data. data can be only one of the following:
value	FeatureValue Feature value.

FetchPublisherModelConfigRequest

Request message for EndpointService.FetchPublisherModelConfig.

Fields
name	string Required. The name of the publisher model, in the format of projects/{project}/locations/{location}/publishers/{publisher}/models/{model}.

FileData

URI-based data.

A FileData message contains a URI pointing to data of a specific media type. It is used to represent images, audio, and video stored in Google Cloud Storage.

Fields
mime_type	string Required. The IANA standard MIME type of the source data.
file_uri	string Required. The URI of the file in Google Cloud Storage.
display_name	string Optional. The display name of the file. Used to provide a label or filename to distinguish files. This field is only returned in PromptMessage for prompt management. It is used in the Gemini calls only when server side tools (code_execution, google_search, and url_context) are enabled.

FileStatus

RagFile status.

Fields
state	State Output only. RagFile state.
error_status	string Output only. Only when the state field is ERROR.

State

RagFile state.

Enums
STATE_UNSPECIFIED	RagFile state is unspecified.
ACTIVE	RagFile resource has been created and indexed successfully.
ERROR	RagFile resource is in a problematic state. See error_message field for details.

FilterSplit

Assigns input data to training, validation, and test sets based on the given filters, data pieces not matched by any filter are ignored. Currently only supported for Datasets containing DataItems. If any of the filters in this message are to match nothing, then they can be set as '-' (the minus sign).

Supported only for unstructured Datasets.

Fields
training_filter	string Required. A filter on DataItems of the Dataset. DataItems that match this filter are used to train the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
validation_filter	string Required. A filter on DataItems of the Dataset. DataItems that match this filter are used to validate the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.
test_filter	string Required. A filter on DataItems of the Dataset. DataItems that match this filter are used to test the Model. A filter with same syntax as the one used in DatasetService.ListDataItems may be used. If a single DataItem is matched by more than one of the FilterSplit filters, then it is assigned to the first set that applies to it in the training, validation, test order.

FlexStart

FlexStart is used to schedule the deployment workload on DWS resource. It contains the max duration of the deployment.

Fields
max_runtime_duration	Duration The max duration of the deployment is max_runtime_duration. The deployment will be terminated after the duration. The max_runtime_duration can be set up to 7 days.

FluencyInput

Input for fluency metric.

Fields
metric_spec	FluencySpec Required. Spec for fluency score metric.
instance	FluencyInstance Required. Fluency instance.

FluencyInstance

Spec for fluency instance.

Fields
prediction	string Required. Output of the evaluated model.

FluencyResult

Spec for fluency result.

Fields
explanation	string Output only. Explanation for fluency score.
score	float Output only. Fluency score.
confidence	float Output only. Confidence for fluency score.

FluencySpec

Spec for fluency score metric.

Fields
version	int32 Optional. Which version to use for evaluation.

FractionSplit

Assigns the input data to training, validation, and test sets as per the given fractions. Any of training_fraction, validation_fraction and test_fraction may optionally be provided, they must sum to up to 1. If the provided ones sum to less than 1, the remainder is assigned to sets as decided by Agent Platform. If none of the fractions are set, by default roughly 80% of data is used for training, 10% for validation, and 10% for test.

Fields
training_fraction	double The fraction of the input data that is to be used to train the Model.
validation_fraction	double The fraction of the input data that is to be used to validate the Model.
test_fraction	double The fraction of the input data that is to be used to evaluate the Model.

FulfillmentInput

Input for fulfillment metric.

Fields
metric_spec	FulfillmentSpec Required. Spec for fulfillment score metric.
instance	FulfillmentInstance Required. Fulfillment instance.

FulfillmentInstance

Spec for fulfillment instance.

Fields
prediction	string Required. Output of the evaluated model.
instruction	string Required. Inference instruction prompt to compare prediction with.

FulfillmentResult

Spec for fulfillment result.

Fields
explanation	string Output only. Explanation for fulfillment score.
score	float Output only. Fulfillment score.
confidence	float Output only. Confidence for fulfillment score.

FulfillmentSpec

Spec for fulfillment metric.

Fields
version	int32 Optional. Which version to use for evaluation.

FullFineTunedResources

Resources for an fft model.

Fields
deployment_type	DeploymentType Required. The kind of deployment.
model_inference_unit_count	int32 Optional. The number of model inference units to use for this deployment. This can only be specified for DEPLOYMENT_TYPE_PROD. The following table lists the number of model inference units for different model types: * Gemini 2.5 Flash * Foundation FMIU: 25 * Expansion FMIU: 4 * Gemini 2.5 Pro * Foundation FMIU: 32 * Expansion FMIU: 16 * Veo 3.0 (undistilled) * Foundation FMIU: 63 * Expansion FMIU: 7 * Veo 3.0 (distilled) * Foundation FMIU: 30 * Expansion FMIU: 10

DeploymentType

The type of deployment.

Enums
DEPLOYMENT_TYPE_UNSPECIFIED	Unspecified deployment type.
DEPLOYMENT_TYPE_EVAL	Eval deployment type.
DEPLOYMENT_TYPE_PROD	Prod deployment type.

FunctionCall

A predicted FunctionCall returned from the model that contains a string representing the FunctionDeclaration.name and a structured JSON object containing the parameters and their values.

Fields
id	string Optional. The unique id of the function call. If populated, the client to execute the function_call and return the response with the matching id.
name	string Optional. The name of the function to call. Matches FunctionDeclaration.name.
args	Struct Optional. The function parameters and values in JSON object format. See FunctionDeclaration.parameters for parameter details.
partial_args[]	PartialArg Optional. The partial argument value of the function call. If provided, represents the arguments/fields that are streamed incrementally.
will_continue	bool Optional. Whether this is the last part of the FunctionCall. If true, another partial message for the current FunctionCall is expected to follow.

FunctionCallingConfig

Function calling config.

Fields
mode	Mode Optional. Function calling mode.
allowed_function_names[]	string Optional. Function names to call. Only set when the Mode is ANY. Function names should match FunctionDeclaration.name. With mode set to ANY, model will predict a function call from the set of function names provided.
stream_function_call_arguments	bool Optional. When set to true, arguments of a single function call will be streamed out in multiple parts/contents/responses. Partial parameter results will be returned in the FunctionCall.partial_args field.

Mode

Function calling mode.

Enums
MODE_UNSPECIFIED	Unspecified function calling mode. This value should not be used.
AUTO	Default model behavior, model decides to predict either function calls or natural language response.
ANY	Model is constrained to always predicting function calls only. If "allowed_function_names" are set, the predicted function calls will be limited to any one of "allowed_function_names", else the predicted function calls will be any one of the provided "function_declarations".
NONE	Model will not predict any function calls. Model behavior is same as when not passing any function declarations.
VALIDATED	Model is constrained to predict either function calls or natural language response. If "allowed_function_names" are set, the predicted function calls will be limited to any one of "allowed_function_names", else the predicted function calls will be any one of the provided "function_declarations".

FunctionDeclaration

Structured representation of a function declaration as defined by the OpenAPI 3.0 specification. Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a Tool by the model and executed by the client.

Fields
name	string Required. The name of the function to call. Must start with a letter or an underscore. Must be a-z, A-Z, 0-9, or contain underscores, dots, colons and dashes, with a maximum length of 128.
description	string Optional. Description and purpose of the function. Model uses it to decide how and whether to call the function.
parameters	Schema Optional. Describes the parameters to this function in JSON Schema Object format. Reflects the Open API 3.03 Parameter Object. string Key: the name of the parameter. Parameter names are case sensitive. Schema Value: the Schema defining the type used for the parameter. For function with no parameters, this can be left unset. Parameter names must start with a letter or an underscore and must only contain chars a-z, A-Z, 0-9, or underscores with a maximum length of 64. Example with 1 required and 1 optional parameter: type: OBJECT properties: param1: type: STRING param2: type: INTEGER required: - param1
parameters_json_schema	Value Optional. Describes the parameters to the function in JSON Schema format. The schema must describe an object where the properties are the parameters to the function. For example: { "type": "object", "properties": { "name": { "type": "string" }, "age": { "type": "integer" } }, "additionalProperties": false, "required": ["name", "age"], "propertyOrdering": ["name", "age"] } This field is mutually exclusive with parameters.
response	Schema Optional. Describes the output from this function in JSON Schema format. Reflects the Open API 3.03 Response Object. The Schema defines the type used for the response value of the function.
response_json_schema	Value Optional. Describes the output from this function in JSON Schema format. The value specified by the schema is the response value of the function. This field is mutually exclusive with response.

FunctionResponse

The result output from a FunctionCall that contains a string representing the FunctionDeclaration.name and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a FunctionCall made based on model prediction.

Fields
id	string Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call id.
name	string Required. The name of the function to call. Matches FunctionDeclaration.name and FunctionCall.name.
response	Struct Required. The function response in JSON object format. Use "output" key to specify function output and "error" key to specify error details (if any). If "output" and "error" keys are not specified, then whole "response" is treated as function output.
parts[]	FunctionResponsePart Optional. Ordered Parts that constitute a function response. Parts may have different IANA MIME types.

FunctionResponseBlob

Raw media bytes for function response.

Text should not be sent as raw bytes, use the 'text' field.

Fields
mime_type	string Required. The IANA standard MIME type of the source data.
data	bytes Required. Raw bytes.
display_name	string Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.

FunctionResponseFileData

URI based data for function response.

Fields
mime_type	string Required. The IANA standard MIME type of the source data.
file_uri	string Required. URI.
display_name	string Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.

FunctionResponsePart

A datatype containing media that is part of a FunctionResponse message.

A FunctionResponsePart consists of data which has an associated datatype. A FunctionResponsePart can only contain one of the accepted types in FunctionResponsePart.data.

A FunctionResponsePart must have a fixed IANA MIME type identifying the type and subtype of the media if the inline_data field is filled with raw bytes.

Fields
Union field data. The data of the function response part. data can be only one of the following:
inline_data	FunctionResponseBlob Inline media bytes.
file_data	FunctionResponseFileData URI based data.

GcsDestination

The Google Cloud Storage location where the output is to be written to.

Fields
output_uri_prefix	string Required. Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.

GcsSource

The Google Cloud Storage location for the input content.

Fields
uris[]	string Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.

GeminiExample

Format for Gemini examples used for Vertex Multimodal datasets.

Fields
model	string Optional. The fully qualified name of the publisher model or tuned model endpoint to use. Publisher model format: projects/{project}/locations/{location}/publishers/*/models/* Tuned model endpoint format: projects/{project}/locations/{location}/endpoints/{endpoint}
contents[]	Content Required. The content of the current conversation with the model. For single-turn queries, this is a single instance. For multi-turn queries, this is a repeated field that contains conversation history + latest request.
cached_content	string Optional. The name of the cached content used as context to serve the prediction. Note: only used in explicit caching, where users can have control over caching (e.g. what content to cache) and enjoy guaranteed cost savings. Format: projects/{project}/locations/{location}/cachedContents/{cachedContent}
tools[]	Tool Optional. A list of Tools the model may use to generate the next response. A Tool is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model.
tool_config	ToolConfig Optional. Tool config. This config is shared for all tools provided in the request.
labels	map<string, string> Optional. The labels with user-defined metadata for the request. It is used for billing and reporting only. Label keys and values can be no longer than 63 characters (Unicode codepoints) and can only contain lowercase letters, numeric characters, underscores, and dashes. International characters are allowed. Label values are optional. Label keys must start with a letter.
safety_settings[]	SafetySetting Optional. Per request settings for blocking unsafe content. Enforced on GenerateContentResponse.candidates.
model_armor_config	ModelArmorConfig Optional. Settings for prompt and response sanitization using the Model Armor service. If supplied, safety_settings must not be supplied.
generation_config	GenerationConfig Optional. Generation config.
system_instruction	Content Optional. The user provided system instructions for the model. Note: only text should be used in parts and content in each part will be in a separate paragraph.

GeminiRequestReadConfig

Configuration for how to read Gemini requests from a multimodal dataset.

Fields
Union field read_config. The read config for the dataset. read_config can be only one of the following:
template_config	GeminiTemplateConfig Gemini request template with placeholders.
assembled_request_column_name	string Optional. Column name in the dataset table that contains already fully assembled Gemini requests.

GeminiTemplateConfig

Template configuration to create Gemini examples from a multimodal dataset.

Fields
gemini_example	GeminiExample Required. The template that will be used for assembling the request to use for downstream applications.
field_mapping	map<string, string> Required. Map of template parameters to the columns in the dataset table.

GenAiAdvancedFeaturesConfig

Configuration for GenAiAdvancedFeatures.

Fields
rag_config	RagConfig Configuration for Retrieval Augmented Generation feature.

RagConfig

Configuration for Retrieval Augmented Generation feature.

Fields
enable_rag	bool If true, enable Retrieval Augmented Generation in ChatCompletion request. Once enabled, the endpoint will be identified as GenAI endpoint and Arthedain router will be used.

GenerateContentRequest

Request message for [PredictionService.GenerateContent].

Fields
model	string Required. The fully qualified name of the publisher model or tuned model endpoint to use. Publisher model format: projects/{project}/locations/{location}/publishers/*/models/* Tuned model endpoint format: projects/{project}/locations/{location}/endpoints/{endpoint}
contents[]	Content Required. The content of the current conversation with the model. For single-turn queries, this is a single instance. For multi-turn queries, this is a repeated field that contains conversation history + latest request.
cached_content	string Optional. The name of the cached content used as context to serve the prediction. Note: only used in explicit caching, where users can have control over caching (e.g. what content to cache) and enjoy guaranteed cost savings. Format: projects/{project}/locations/{location}/cachedContents/{cachedContent}
tools[]	Tool Optional. A list of Tools the model may use to generate the next response. A Tool is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model.
tool_config	ToolConfig Optional. Tool config. This config is shared for all tools provided in the request.
labels	map<string, string> Optional. The labels with user-defined metadata for the request. It is used for billing and reporting only. Label keys and values can be no longer than 63 characters (Unicode codepoints) and can only contain lowercase letters, numeric characters, underscores, and dashes. International characters are allowed. Label values are optional. Label keys must start with a letter.
safety_settings[]	SafetySetting Optional. Per request settings for blocking unsafe content. Enforced on GenerateContentResponse.candidates.
model_armor_config	ModelArmorConfig Optional. Settings for prompt and response sanitization using the Model Armor service. If supplied, safety_settings must not be supplied.
generation_config	GenerationConfig Optional. Generation config.
system_instruction	Content Optional. The user provided system instructions for the model. Note: only text should be used in parts and content in each part will be in a separate paragraph.

GenerateContentResponse

Response message for [PredictionService.GenerateContent].

Fields
candidates[]	Candidate Output only. Generated candidates.
model_version	string Output only. The model version used to generate the response.
create_time	Timestamp Output only. Timestamp when the request is made to the server.
response_id	string Output only. response_id is used to identify each response. It is the encoding of the event_id.
prompt_feedback	PromptFeedback Output only. Content filter results for a prompt sent in the request. Note: Sent only in the first stream chunk. Only happens when no candidates were generated due to content violations.
usage_metadata	UsageMetadata Usage metadata about the response(s).

PromptFeedback

Content filter results for a prompt sent in the request. Note: This is sent only in the first stream chunk and only if no candidates were generated due to content violations.

Fields
block_reason	BlockedReason Output only. The reason why the prompt was blocked.
safety_ratings[]	SafetyRating Output only. A list of safety ratings for the prompt. There is one rating per category.
block_reason_message	string Output only. A readable message that explains the reason why the prompt was blocked.

BlockedReason

The reason why the prompt was blocked.

Enums
BLOCKED_REASON_UNSPECIFIED	The blocked reason is unspecified.
SAFETY	The prompt was blocked for safety reasons.
OTHER	The prompt was blocked for other reasons. For example, it may be due to the prompt's language, or because it contains other harmful content.
BLOCKLIST	The prompt was blocked because it contains a term from the terminology blocklist.
PROHIBITED_CONTENT	The prompt was blocked because it contains prohibited content.
MODEL_ARMOR	The prompt was blocked by Model Armor.
IMAGE_SAFETY	The prompt was blocked because it contains content that is unsafe for image generation.
JAILBREAK	The prompt was blocked as a jailbreak attempt.

UsageMetadata

Usage metadata about the content generation request and response. This message provides a detailed breakdown of token usage and other relevant metrics.

Fields
prompt_token_count	int32 The total number of tokens in the prompt. This includes any text, images, or other media provided in the request. When cached_content is set, this also includes the number of tokens in the cached content.
candidates_token_count	int32 The total number of tokens in the generated candidates.
total_token_count	int32 The total number of tokens for the entire request. This is the sum of prompt_token_count, candidates_token_count, tool_use_prompt_token_count, and thoughts_token_count.
tool_use_prompt_token_count	int32 Output only. The number of tokens in the results from tool executions, which are provided back to the model as input, if applicable.
thoughts_token_count	int32 Output only. The number of tokens that were part of the model's generated "thoughts" output, if applicable.
cached_content_token_count	int32 Output only. The number of tokens in the cached content that was used for this request.
prompt_tokens_details[]	ModalityTokenCount Output only. A detailed breakdown of the token count for each modality in the prompt.
cache_tokens_details[]	ModalityTokenCount Output only. A detailed breakdown of the token count for each modality in the cached content.
candidates_tokens_details[]	ModalityTokenCount Output only. A detailed breakdown of the token count for each modality in the generated candidates.
tool_use_prompt_tokens_details[]	ModalityTokenCount Output only. A detailed breakdown by modality of the token counts from the results of tool executions, which are provided back to the model as input.
traffic_type	TrafficType Output only. The traffic type for this request.

TrafficType

The type of traffic that this request was processed with, indicating which quota is consumed.

Enums
TRAFFIC_TYPE_UNSPECIFIED	Unspecified request traffic type.
ON_DEMAND	The request was processed using Pay-As-You-Go quota.
ON_DEMAND_PRIORITY	Type for Priority Pay-As-You-Go traffic.
ON_DEMAND_FLEX	Type for Flex traffic.
PROVISIONED_THROUGHPUT	Type for Provisioned Throughput traffic.

GenerateFetchAccessTokenRequest

Request message for FeatureOnlineStoreService.GenerateFetchAccessToken.

Fields
feature_view	string FeatureView resource format projects/{project}/locations/{location}/featureOnlineStores/{featureOnlineStore}/featureViews/{featureView}

GenerateFetchAccessTokenResponse

Response message for FeatureOnlineStoreService.GenerateFetchAccessToken.

Fields
access_token	string The OAuth 2.0 access token.
expire_time	Timestamp Token expiration time. This is always set

GenerateInstanceRubricsRequest

Request message for EvaluationService.GenerateInstanceRubrics.

Fields
location	string Required. The resource name of the Location to generate rubrics from. Format: projects/{project}/locations/{location}
contents[]	Content Required. The prompt to generate rubrics from. For single-turn queries, this is a single instance. For multi-turn queries, this is a repeated field that contains conversation history + latest request.
predefined_rubric_generation_spec	PredefinedMetricSpec Optional. Specification for using the rubric generation configs of a pre-defined metric, e.g. "generic_quality_v1" and "instruction_following_v1". Some of the configs may be only used in rubric generation and not supporting evaluation, e.g. "fully_customized_generic_quality_v1". If this field is set, the rubric_generation_spec field will be ignored.
rubric_generation_spec	RubricGenerationSpec Optional. Specification for how the rubrics should be generated.
agent_config	DeprecatedAgentConfig Optional. Agent configuration, required for agent-based rubric generation.
metric_resource_name	string Optional. The resource name of a registered metric. Rubric generation using predefined metric spec or LLMBasedMetricSpec is supported. If this field is set, the configuration provided in this field is used for rubric generation. The predefined_rubric_generation_spec and rubric_generation_spec fields will be ignored.

GenerateInstanceRubricsResponse

Response message for EvaluationService.GenerateInstanceRubrics.

Fields
generated_rubrics[]	Rubric Output only. A list of generated rubrics.

GenerateLossClustersOperationMetadata

Operation metadata for EvaluationAnalyticsService.GenerateLossClusters.

Fields
generic_metadata	GenericOperationMetadata Generic operation metadata.

GenerateLossClustersRequest

Request message for EvaluationAnalyticsService.GenerateLossClusters.

Fields
location	string Required. The resource name of the Location. Format: projects/{project}/locations/{location}
configs[]	LossAnalysisConfig Required. Configuration for the analysis algorithm. Analysis for multiple metrics and multiple candidates could be specified.
Union field source. The source of evaluation data to analyze. source can be only one of the following:
evaluation_set	string Reference to a persisted EvaluationSet. The service will read items from this set.
inline_results	EvaluationResultList Inline evaluation results. Useful for ephemeral analysis in notebooks/SDKs where data isn't persisted.

EvaluationResultList

This type has no fields.

A wrapper to allow providing a list of items inline.

GenerateLossClustersResponse

Response message for EvaluationAnalyticsService.GenerateLossClusters.

Fields
analysis_time	Timestamp The timestamp when this analysis was completed.
results[]	LossAnalysisResult The analysis results, one per config provided in the request.

GenerateMemoriesOperationMetadata

Details of MemoryBankService.GenerateMemories operation.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

GenerateMemoriesRequest

Request message for MemoryBankService.GenerateMemories. Maximum size is 8 MB.

Fields
parent	string Required. The resource name of the ReasoningEngine to generate memories for. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}
disable_consolidation	bool Optional. If true, generated memories will not be consolidated with existing memories; all generated memories will be added as new memories regardless of whether they are duplicates of or contradictory to existing memories. By default, memory consolidation is enabled.
scope	map<string, string> Optional. The scope of the memories that should be generated. Memories will be consolidated across memories with the same scope. Must be provided unless the scope is defined in the source content. If scope is provided, it will override the scope defined in the source content. Scope values cannot contain the wildcard character '*'.
Union field source. Source content used to generate memories. source can be only one of the following:
vertex_session_source	VertexSessionSource Defines a Vertex Session as the source content from which to generate memories.
direct_contents_source	DirectContentsSource Defines a direct source of content as the source content from which to generate memories.
direct_memories_source	DirectMemoriesSource Defines a direct source of memories that should be uploaded to Memory Bank. This is similar to CreateMemory, but it allows for consolidation between these new memories and existing memories for the same scope.
Union field revision_expiration. The expiration of the Memory Revisions created as a result of this request. If not set, Memory Bank will defer to MemoryBankConfig.memory_revision_default_ttl or the global default, 365 days. revision_expiration can be only one of the following:
revision_expire_time	Timestamp Optional. Timestamp of when the revision is considered expired. If not set, the memory revision will be kept until manually deleted.
revision_ttl	Duration Optional. The TTL for the revision. The expiration time is computed: now + TTL.

DirectContentsSource

Defines a direct source of content from which to generate the memories.

Fields
events[]	Event Required. The source content (i.e. chat history) to generate memories from.

Event

A single piece of conversation from which to generate memories.

Fields
content	Content Required. A single piece of content from which to generate memories.

DirectMemoriesSource

Defines a direct source of memories that should be uploaded to Memory Bank with consolidation.

Fields
direct_memories[]	DirectMemory Required. The direct memories to upload to Memory Bank. At most 5 direct memories are allowed per request.

DirectMemory

A direct memory to upload to Memory Bank.

Fields
fact	string Required. The fact to consolidate with existing memories.

VertexSessionSource

Defines an Agent Engine Session from which to generate the memories. If scope is not provided, the scope will be extracted from the Session (i.e. {"user_id": sesison.user_id}).

Fields
session	string Required. The resource name of the Session to generate memories for. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}/sessions/{session}
start_time	Timestamp Optional. Time range to define which session events should be used to generate memories. Start time (inclusive) of the time range. If not set, the start time is unbounded.
end_time	Timestamp Optional. End time (exclusive) of the time range. If not set, the end time is unbounded.

GenerateMemoriesResponse

Response message for MemoryBankService.GenerateMemories.

Fields
generated_memories[]	GeneratedMemory The generated memories.

GeneratedMemory

A memory generated by the operation.

Fields
memory	Memory The generated Memory.
action	Action The action that was performed on the Memory.

Action

Actions that can be performed on a Memory.

Enums
ACTION_UNSPECIFIED	Action is unspecified.
CREATED	The memory was created.
UPDATED	The memory was updated. The fact field may not be updated if the existing fact is still accurate.
DELETED	The memory was deleted.

GenerateSyntheticDataRequest

Request message for DataFoundryService.GenerateSyntheticData. It contains the settings and information needed to generate synthetic data.

Fields
location	string Required. The geographic location where the synthetic data generation request is processed. This should be in the format projects/{project}/locations/{location}. For example, projects/my-project/locations/us-central1.
count	int32 Required. The number of synthetic examples to generate. For this stateless API, you can generate up to 50 examples in a single request.
output_field_specs[]	OutputFieldSpec Required. Defines the schema of each synthetic example to be generated, defined by a list of fields.
examples[]	SyntheticExample Optional. A list of few-shot examples that help the model understand the desired style, tone, and format of the generated synthetic data. Providing these few-shot examples can significantly improve the quality and relevance of the output.
Union field strategy. Specifies how the synthetic data should be generated. Choose one of the available strategies. strategy can be only one of the following:
task_description	TaskDescriptionStrategy Generates synthetic data based on a high-level description of the task or data you want.

GenerateSyntheticDataResponse

The response message for the GenerateSyntheticData method, containing the synthetic examples generated by the Gen AI evaluation service.

Fields
synthetic_examples[]	SyntheticExample A list of generated synthetic examples, each containing a complete synthetic data instance generated based on your request.

GenerateVideoResponse

Generate video response.

Fields
generated_samples[] (deprecated)	string This item is deprecated! The cloud storage uris of the generated videos.
rai_media_filtered_reasons[]	string Returns rai failure reasons if any.
rai_media_filtered_count	int32 Returns if any videos were filtered due to RAI policies.

GenerationConfig

Configuration for content generation.

This message contains all the parameters that control how the model generates content. It allows you to influence the randomness, length, and structure of the output.

Fields
stop_sequences[]	string Optional. A list of character sequences that will stop the model from generating further tokens. If a stop sequence is generated, the output will end at that point. This is useful for controlling the length and structure of the output. For example, you can use ["\n", "###"] to stop generation at a new line or a specific marker.
response_mime_type	string Optional. The IANA standard MIME type of the response. The model will generate output that conforms to this MIME type. Supported values include 'text/plain' (default) and 'application/json'. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined.
response_modalities[]	Modality Optional. The modalities of the response. The model will generate a response that includes all the specified modalities. For example, if this is set to [TEXT, IMAGE], the response will include both text and an image.
thinking_config	ThinkingConfig Optional. Configuration for thinking features. An error will be returned if this field is set for models that don't support thinking.
model_config (deprecated)	ModelConfig Optional. The model_config field is deprecated and is not supported anymore. Use routing_config instead. Optional. Config for model selection.
temperature	float Optional. Controls the randomness of the output. A higher temperature results in more creative and diverse responses, while a lower temperature makes the output more predictable and focused. The valid range is (0.0, 2.0].
top_p	float Optional. Specifies the nucleus sampling threshold. The model considers only the smallest set of tokens whose cumulative probability is at least top_p. This helps generate more diverse and less repetitive responses. For example, a top_p of 0.9 means the model considers tokens until the cumulative probability of the tokens to select from reaches 0.9. It's recommended to adjust either temperature or top_p, but not both.
top_k	float Optional. Specifies the top-k sampling threshold. The model considers only the top k most probable tokens for the next token. This can be useful for generating more coherent and less random text. For example, a top_k of 40 means the model will choose the next word from the 40 most likely words.
candidate_count	int32 Optional. The number of candidate responses to generate. A higher candidate_count can provide more options to choose from, but it also consumes more resources. This can be useful for generating a variety of responses and selecting the best one.
max_output_tokens	int32 Optional. The maximum number of tokens to generate in the response. A token is approximately four characters. The default value varies by model. This parameter can be used to control the length of the generated text and prevent overly long responses.
response_logprobs	bool Optional. If set to true, the log probabilities of the output tokens are returned. Log probabilities are the logarithm of the probability of a token appearing in the output. A higher log probability means the token is more likely to be generated. This can be useful for analyzing the model's confidence in its own output and for debugging.
logprobs	int32 Optional. The number of top log probabilities to return for each token. This can be used to see which other tokens were considered likely candidates for a given position. A higher value will return more options, but it will also increase the size of the response.
presence_penalty	float Optional. Penalizes tokens that have already appeared in the generated text. A positive value encourages the model to generate more diverse and less repetitive text. Valid values can range from [-2.0, 2.0].
frequency_penalty	float Optional. Penalizes tokens based on their frequency in the generated text. A positive value helps to reduce the repetition of words and phrases. Valid values can range from [-2.0, 2.0].
seed	int32 Optional. A seed for the random number generator. By setting a seed, you can make the model's output mostly deterministic. For a given prompt and parameters (like temperature, top_p, etc.), the model will produce the same response every time. However, it's not a guaranteed absolute deterministic behavior. This is different from parameters like temperature, which control the level of randomness. seed ensures that the "random" choices the model makes are the same on every run, making it essential for testing and ensuring reproducible results.
response_schema	Schema Optional. Lets you to specify a schema for the model's response, ensuring that the output conforms to a particular structure. This is useful for generating structured data such as JSON. The schema is a subset of the OpenAPI 3.0 schema object object. When this field is set, you must also set the response_mime_type to application/json.
response_json_schema	Value Optional. When this field is set, response_schema must be omitted and response_mime_type must be set to application/json.
routing_config	RoutingConfig Optional. Routing configuration.
audio_timestamp	bool Optional. If enabled, audio timestamps will be included in the request to the model. This can be useful for synchronizing audio with other modalities in the response.
media_resolution	MediaResolution Optional. The token resolution at which input media content is sampled. This is used to control the trade-off between the quality of the response and the number of tokens used to represent the media. A higher resolution allows the model to perceive more detail, which can lead to a more nuanced response, but it will also use more tokens. This does not affect the image dimensions sent to the model.
speech_config	SpeechConfig Optional. The speech generation config.
enable_affective_dialog	bool Optional. If enabled, the model will detect emotions and adapt its responses accordingly. For example, if the model detects that the user is frustrated, it may provide a more empathetic response.
image_config	ImageConfig Optional. Config for image generation features.

MediaResolution

Media resolution for the input media.

Enums
MEDIA_RESOLUTION_UNSPECIFIED	Media resolution has not been set.
MEDIA_RESOLUTION_LOW	Media resolution set to low (64 tokens).
MEDIA_RESOLUTION_MEDIUM	Media resolution set to medium (256 tokens).
MEDIA_RESOLUTION_HIGH	Media resolution set to high (zoomed reframing with 256 tokens).

Modality

The modalities of the response.

Enums
MODALITY_UNSPECIFIED	Unspecified modality. Will be processed as text.
TEXT	Text modality.
IMAGE	Image modality.
AUDIO	Audio modality.
VIDEO	Video modality.

ModelConfig

Config for model selection.

Fields
feature_selection_preference	FeatureSelectionPreference Required. Feature selection preference.

FeatureSelectionPreference

Options for feature selection preference.

Enums
FEATURE_SELECTION_PREFERENCE_UNSPECIFIED	Unspecified feature selection preference.
PRIORITIZE_QUALITY	Prefer higher quality over lower cost.
BALANCED	Balanced feature selection preference.
PRIORITIZE_COST	Prefer lower cost over higher quality.

RoutingConfig

The configuration for routing the request to a specific model. This can be used to control which model is used for the generation, either automatically or by specifying a model name.

Fields
Union field routing_config. The routing mode for the request. routing_config can be only one of the following:
auto_mode	AutoRoutingMode In this mode, the model is selected automatically based on the content of the request.
manual_mode	ManualRoutingMode In this mode, the model is specified manually.

AutoRoutingMode

The configuration for automated routing.

When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference.

Fields
model_routing_preference	ModelRoutingPreference The model routing preference.

ModelRoutingPreference

The model routing preference.

Enums
UNKNOWN	Unspecified model routing preference.
PRIORITIZE_QUALITY	The model will be selected to prioritize the quality of the response.
BALANCED	The model will be selected to balance quality and cost.
PRIORITIZE_COST	The model will be selected to prioritize the cost of the request.

ManualRoutingMode

The configuration for manual routing.

When manual routing is specified, the model will be selected based on the model name provided.

Fields
model_name	string The name of the model to use. Only public LLM models are accepted.

ThinkingConfig

Configuration for the model's thinking features.

"Thinking" is a process where the model breaks down a complex task into smaller, manageable steps. This allows the model to reason about the task, plan its approach, and execute the plan to generate a high-quality response.

Fields
include_thoughts	bool Optional. If true, the model will include its thoughts in the response. "Thoughts" are the intermediate steps the model takes to arrive at the final response. They can provide insights into the model's reasoning process and help with debugging. If this is true, thoughts are returned only when available.
thinking_budget	int32 Optional. The token budget for the model's thinking process. The model will make a best effort to stay within this budget. This can be used to control the trade-off between response quality and latency.
thinking_level	ThinkingLevel Optional. The number of thoughts tokens that the model should generate.

ThinkingLevel

The thinking level for the model.

Enums
THINKING_LEVEL_UNSPECIFIED	Unspecified thinking level.
LOW	Low thinking level.
MEDIUM	Medium thinking level.
HIGH	High thinking level.
MINIMAL	MINIMAL thinking level.

GenericOperationMetadata

Generic Metadata shared by all operations.

Fields
partial_failures[]	Status Output only. Partial failures encountered. E.g. single files that couldn't be read. This field should never exceed 20 entries. Status details field will contain standard Google Cloud error details.
create_time	Timestamp Output only. Time when the operation was created.
update_time	Timestamp Output only. Time when the operation was updated for the last time. If the operation has finished (successfully or not), this is the finish time.

GenieSource

Contains information about the source of the models generated from Generative AI Studio.

Fields
base_model_uri	string Required. The public base model URI.

GetAgentRequest

Request message for AgentService.GetAgent.

Fields
name	string Required. The resource name of the agent to retrieve. Format: projects/{project}/locations/{location}/agents/{agent}.

GetAnnotationSpecRequest

Request message for DatasetService.GetAnnotationSpec.

Fields
name	string Required. The name of the AnnotationSpec resource. Format: projects/{project}/locations/{location}/datasets/{dataset}/annotationSpecs/{annotation_spec}
read_mask	FieldMask Mask specifying which fields to read.

GetArtifactRequest

Request message for MetadataService.GetArtifact.

Fields
name	string Required. The resource name of the Artifact to retrieve. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/artifacts/{artifact}

GetBatchPredictionJobRequest

Request message for JobService.GetBatchPredictionJob.

Fields
name	string Required. The name of the BatchPredictionJob resource. Format: projects/{project}/locations/{location}/batchPredictionJobs/{batch_prediction_job}

GetCachedContentRequest

Request message for GenAiCacheService.GetCachedContent.

Fields
name	string Required. The resource name referring to the cached content

GetContextRequest

Request message for MetadataService.GetContext.

Fields
name	string Required. The resource name of the Context to retrieve. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/contexts/{context}

GetCustomJobRequest

Request message for JobService.GetCustomJob.

Fields
name	string Required. The name of the CustomJob resource. Format: projects/{project}/locations/{location}/customJobs/{custom_job}

GetDatasetRequest

Request message for DatasetService.GetDataset.

Fields
name	string Required. The name of the Dataset resource.
read_mask	FieldMask Mask specifying which fields to read.

GetDatasetVersionRequest

Request message for DatasetService.GetDatasetVersion.

Fields
name	string Required. The resource name of the Dataset version to delete. Format: projects/{project}/locations/{location}/datasets/{dataset}/datasetVersions/{dataset_version}
read_mask	FieldMask Mask specifying which fields to read.

GetDeploymentResourcePoolRequest

Request message for GetDeploymentResourcePool method.

Fields
name	string Required. The name of the DeploymentResourcePool to retrieve. Format: projects/{project}/locations/{location}/deploymentResourcePools/{deployment_resource_pool}

GetEndpointRequest

Request message for EndpointService.GetEndpoint

Fields
name	string Required. The name of the Endpoint resource. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

GetEntityTypeRequest

Request message for FeaturestoreService.GetEntityType.

Fields
name	string Required. The name of the EntityType resource. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type}

GetExampleStoreRequest

Request message for ExampleStoreService.GetExampleStore.

Fields
name	string Required. The resource name of the ExampleStore. Format: projects/{project}/locations/{location}/exampleStores/{example_store}

GetExecutionRequest

Request message for MetadataService.GetExecution.

Fields
name	string Required. The resource name of the Execution to retrieve. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/executions/{execution}

GetExtensionRequest

Request message for ExtensionRegistryService.GetExtension.

Fields
name	string Required. The name of the Extension resource. Format: projects/{project}/locations/{location}/extensions/{extension}

GetFeatureGroupRequest

Request message for FeatureRegistryService.GetFeatureGroup.

Fields
name	string Required. The name of the FeatureGroup resource.

GetFeatureMonitorJobRequest

Request message for FeatureRegistryService.GetFeatureMonitorJob.

Fields
name	string Required. The name of the FeatureMonitorJob resource. Format: projects/{project}/locations/{location}/featureGroups/{feature_group}/featureMonitors/{feature_monitor}/featureMonitorJobs/{feature_monitor_job}

GetFeatureMonitorRequest

Request message for FeatureRegistryService.GetFeatureMonitor.

Fields
name	string Required. The name of the FeatureMonitor resource.

GetFeatureOnlineStoreRequest

Request message for FeatureOnlineStoreAdminService.GetFeatureOnlineStore.

Fields
name	string Required. The name of the FeatureOnlineStore resource.

GetFeatureRequest

Request message for FeaturestoreService.GetFeature. Request message for FeatureRegistryService.GetFeature.

Fields
name	string Required. The name of the Feature resource. Format for entity_type as parent: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} Format for feature_group as parent: projects/{project}/locations/{location}/featureGroups/{feature_group}
feature_stats_and_anomaly_spec	FeatureStatsAndAnomalySpec Optional. Only applicable for Agent Platform Feature Store. If set, retrieves FeatureStatsAndAnomaly generated by FeatureMonitors based on this spec.

GetFeatureViewRequest

Request message for FeatureOnlineStoreAdminService.GetFeatureView.

Fields
name	string Required. The name of the FeatureView resource. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}

GetFeatureViewSyncRequest

Request message for FeatureOnlineStoreAdminService.GetFeatureViewSync.

Fields
name	string Required. The name of the FeatureViewSync resource. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}/featureViewSyncs/{feature_view_sync}

GetFeaturestoreRequest

Request message for FeaturestoreService.GetFeaturestore.

Fields
name	string Required. The name of the Featurestore resource.

GetHyperparameterTuningJobRequest

Request message for JobService.GetHyperparameterTuningJob.

Fields
name	string Required. The name of the HyperparameterTuningJob resource. Format: projects/{project}/locations/{location}/hyperparameterTuningJobs/{hyperparameter_tuning_job}

GetIndexEndpointRequest

Request message for IndexEndpointService.GetIndexEndpoint

Fields
name	string Required. The name of the IndexEndpoint resource. Format: projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}

GetIndexRequest

Request message for IndexService.GetIndex

Fields
name	string Required. The name of the Index resource. Format: projects/{project}/locations/{location}/indexes/{index}

GetMemoryRequest

Request message for MemoryBankService.GetMemory.

Fields
name	string Required. The resource name of the Memory. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}/memories/{memory}

GetMetadataSchemaRequest

Request message for MetadataService.GetMetadataSchema.

Fields
name	string Required. The resource name of the MetadataSchema to retrieve. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}/metadataSchemas/{metadataschema}

GetMetadataStoreRequest

Request message for MetadataService.GetMetadataStore.

Fields
name	string Required. The resource name of the MetadataStore to retrieve. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}

GetModelDeploymentMonitoringJobRequest

Request message for JobService.GetModelDeploymentMonitoringJob.

Fields
name	string Required. The resource name of the ModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}/modelDeploymentMonitoringJobs/{model_deployment_monitoring_job}

GetModelEvaluationRequest

Request message for ModelService.GetModelEvaluation.

Fields
name	string Required. The name of the ModelEvaluation resource. Format: projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}

GetModelEvaluationSliceRequest

Request message for ModelService.GetModelEvaluationSlice.

Fields
name	string Required. The name of the ModelEvaluationSlice resource. Format: projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}/slices/{slice}

GetModelMonitorRequest

Request message for ModelMonitoringService.GetModelMonitor.

Fields
name	string Required. The name of the ModelMonitor resource. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}

GetModelMonitoringJobRequest

Request message for ModelMonitoringService.GetModelMonitoringJob.

Fields
name	string Required. The resource name of the ModelMonitoringJob. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}/modelMonitoringJobs/{model_monitoring_job}

GetModelRequest

Request message for ModelService.GetModel.

Fields

name

string Required. The name of the Model resource. Format: projects/{project}/locations/{location}/models/{model} In order to retrieve a specific version of the model, also provide the version ID or version alias. Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden If no version ID or alias is specified, the "default" version will be returned. The "default" version alias is created for the first version of the model, and can be moved to other versions later on. There will be exactly one default version.

GetNotebookExecutionJobRequest

Request message for [NotebookService.GetNotebookExecutionJob]

Fields
name	string Required. The name of the NotebookExecutionJob resource.
view	NotebookExecutionJobView Optional. The NotebookExecutionJob view. Defaults to BASIC.

GetNotebookRuntimeRequest

Request message for NotebookService.GetNotebookRuntime

Fields
name	string Required. The name of the NotebookRuntime resource. Instead of checking whether the name is in valid NotebookRuntime resource name format, directly throw NotFound exception if there is no such NotebookRuntime in spanner.

GetNotebookRuntimeTemplateRequest

Request message for NotebookService.GetNotebookRuntimeTemplate

Fields
name	string Required. The name of the NotebookRuntimeTemplate resource. Format: projects/{project}/locations/{location}/notebookRuntimeTemplates/{notebook_runtime_template}

GetOnlineEvaluatorRequest

Request message for GetOnlineEvaluator.

Fields
name	string Required. The name of the OnlineEvaluator to retrieve. Format: projects/{project}/locations/{location}/onlineEvaluators/{id}.

GetPersistentResourceRequest

Request message for PersistentResourceService.GetPersistentResource.

Fields
name	string Required. The name of the PersistentResource resource. Format: projects/{project_id_or_number}/locations/{location_id}/persistentResources/{persistent_resource_id}

GetPipelineJobRequest

Request message for PipelineService.GetPipelineJob.

Fields
name	string Required. The name of the PipelineJob resource. Format: projects/{project}/locations/{location}/pipelineJobs/{pipeline_job}

GetPublisherModelRequest

Request message for ModelGardenService.GetPublisherModel

Fields
name	string Required. The name of the PublisherModel resource. Format: publishers/{publisher}/models/{publisher_model}
language_code	string Optional. The IETF BCP-47 language code representing the language in which the publisher model's text information should be written in.
view	PublisherModelView Optional. PublisherModel view specifying which fields to read.
is_hugging_face_model	bool Optional. Boolean indicates whether the requested model is a Hugging Face model.
hugging_face_token	string Optional. Token used to access Hugging Face gated models.
include_equivalent_model_garden_model_deployment_configs	bool Optional. Whether to cnclude the deployment configs from the equivalent Model Garden model if the requested model is a Hugging Face model.

GetRagCorpusRequest

Request message for VertexRagDataService.GetRagCorpus

Fields
name	string Required. The name of the RagCorpus resource. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}

GetRagDataSchemaRequest

Request message for VertexRagDataService.GetRagDataSchema

Fields
name	string Required. The name of the RagDataSchema resource. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}/ragDataSchemas/{rag_data_schema}

GetRagEngineConfigRequest

Request message for VertexRagDataService.GetRagEngineConfig

Fields
name	string Required. The name of the RagEngineConfig resource. Format: projects/{project}/locations/{location}/ragEngineConfig

GetRagFileRequest

Request message for VertexRagDataService.GetRagFile

Fields
name	string Required. The name of the RagFile resource. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}/ragFiles/{rag_file}

GetRagMetadataRequest

Request message for VertexRagDataService.GetRagMetadata

Fields
name	string Required. The name of the RagMetadata resource. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}/ragFiles/{rag_file}/ragMetadata/{rag_metadata}

GetReasoningEngineRequest

Request message for ReasoningEngineService.GetReasoningEngine.

Fields
name	string Required. The name of the ReasoningEngine resource. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}

GetReasoningEngineRuntimeRevisionRequest

Request message for ReasoningEngineRuntimeRevisionService.GetReasoningEngineRuntimeRevision.

Fields
name	string Required. The name of the ReasoningEngineRuntimeRevision resource. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}/runtimeRevisions/{runtimeRevision}

GetResponseRequest

Request message for PredictionService.GetResponse.

Fields
name	string Required. The name of the Response resource. Format: projects/{project}/locations/{location}/endpoints/{endpoint}/responses/{response}

GetScheduleRequest

Request message for ScheduleService.GetSchedule.

Fields
name	string Required. The name of the Schedule resource. Format: projects/{project}/locations/{location}/schedules/{schedule}

GetSessionRequest

Request message for SessionService.GetSession.

Fields
name	string Required. The resource name of the session. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}/sessions/{session}

GetSkillRequest

Request message for SkillRegistryService.GetSkill.

Fields
name	string Required. The resource name of the Skill to retrieve. Format: projects/{project}/locations/{location}/skills/{skill}

GetSkillRevisionRequest

Request message for SkillRegistryService.GetSkillRevision.

Fields
name	string Required. The resource name of the Skill Revision to retrieve. Format: projects/{project}/locations/{location}/skills/{skill}/revisions/{revision}

GetSpecialistPoolRequest

Request message for SpecialistPoolService.GetSpecialistPool.

Fields
name	string Required. The name of the SpecialistPool resource. The form is projects/{project}/locations/{location}/specialistPools/{specialist_pool}.

GetStudyRequest

Request message for VizierService.GetStudy.

Fields
name	string Required. The name of the Study resource. Format: projects/{project}/locations/{location}/studies/{study}

GetTensorboardExperimentRequest

Request message for TensorboardService.GetTensorboardExperiment.

Fields
name	string Required. The name of the TensorboardExperiment resource. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}

GetTensorboardRequest

Request message for TensorboardService.GetTensorboard.

Fields
name	string Required. The name of the Tensorboard resource. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

GetTensorboardRunRequest

Request message for TensorboardService.GetTensorboardRun.

Fields
name	string Required. The name of the TensorboardRun resource. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}

GetTensorboardTimeSeriesRequest

Request message for TensorboardService.GetTensorboardTimeSeries.

Fields
name	string Required. The name of the TensorboardTimeSeries resource. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}/timeSeries/{time_series}

GetTrainingPipelineRequest

Request message for PipelineService.GetTrainingPipeline.

Fields
name	string Required. The name of the TrainingPipeline resource. Format: projects/{project}/locations/{location}/trainingPipelines/{training_pipeline}

GetTrialRequest

Request message for VizierService.GetTrial.

Fields
name	string Required. The name of the Trial resource. Format: projects/{project}/locations/{location}/studies/{study}/trials/{trial}

GetTuningJobRequest

Request message for GenAiTuningService.GetTuningJob.

Fields
name	string Required. The name of the tuning job to retrieve. Format: projects/{project}/locations/{location}/tuningJobs/{tuning_job}

GoogleDriveSource

The Google Drive location for the input content.

Fields
resource_ids[]	ResourceId Required. Google Drive resource IDs.

ResourceId

The type and ID of the Google Drive resource.

Fields
resource_type	ResourceType Required. The type of the Google Drive resource.
resource_id	string Required. The ID of the Google Drive resource.

ResourceType

The type of the Google Drive resource.

Enums
RESOURCE_TYPE_UNSPECIFIED	Unspecified resource type.
RESOURCE_TYPE_FILE	File resource type.
RESOURCE_TYPE_FOLDER	Folder resource type.

GoogleMaps

Tool to retrieve public maps data for grounding, powered by Google.

Fields
enable_widget	bool Optional. If true, include the widget context token in the response.

GoogleSearchRetrieval

Tool to retrieve public web data for grounding, powered by Google.

Fields
dynamic_retrieval_config	DynamicRetrievalConfig Specifies the dynamic retrieval configuration for the given source.

GroundednessInput

Input for groundedness metric.

Fields
metric_spec	GroundednessSpec Required. Spec for groundedness metric.
instance	GroundednessInstance Required. Groundedness instance.

GroundednessInstance

Spec for groundedness instance.

Fields
prediction	string Required. Output of the evaluated model.
context	string Required. Background information provided in context used to compare against the prediction.

GroundednessResult

Spec for groundedness result.

Fields
explanation	string Output only. Explanation for groundedness score.
score	float Output only. Groundedness score.
confidence	float Output only. Confidence for groundedness score.

GroundednessSpec

Spec for groundedness metric.

Fields
version	int32 Optional. Which version to use for evaluation.

GroundingChunk

A piece of evidence that supports a claim made by the model.

This is used to show a citation for a claim made by the model. When grounding is enabled, the model returns a GroundingChunk that contains a reference to the source of the information.

Fields
Union field chunk_type. The source of the grounding chunk, which can be from Google Search, Agent Platform Search, or Google Maps. chunk_type can be only one of the following:
web	Web A grounding chunk from a web page, typically from Google Search. See the Web message for details.
retrieved_context	RetrievedContext A grounding chunk from a data source retrieved by a retrieval tool, such as Agent Platform Search. See the RetrievedContext message for details
maps	Maps A grounding chunk from Google Maps. See the Maps message for details.

Maps

A Maps chunk is a piece of evidence that comes from Google Maps, containing information about places or routes. This is used to provide the user with rich, location-based information.

Fields
place_answer_sources	PlaceAnswerSources The sources that were used to generate the place answer. This includes review snippets and photos that were used to generate the answer, as well as URIs to flag content.
uri	string The URI of the place.
title	string The title of the place.
text	string The text of the place answer.
place_id	string This Place's resource name, in places/{place_id} format. This can be used to look up the place in the Google Maps API.

PlaceAnswerSources

The sources that were used to generate the place answer. This includes review snippets and photos that were used to generate the answer, as well as URIs to flag content.

Fields
review_snippets[]	ReviewSnippet Snippets of reviews that were used to generate the answer.

ReviewSnippet

A review snippet that is used to generate the answer.

Fields
review_id	string The ID of the review that is being referenced.
google_maps_uri	string A link to show the review on Google Maps.
title	string The title of the review.

RetrievedContext

Context retrieved from a data source to ground the model's response. This is used when a retrieval tool fetches information from a user-provided corpus or a public dataset.

Fields
Union field context_details. Provides tool-specific details about the retrieved context. This allows for different types of retrieval tools to return their own specific metadata. context_details can be only one of the following:
rag_chunk	RagChunk Additional context for a Retrieval-Augmented Generation (RAG) retrieval result. This is populated only when the RAG retrieval tool is used.
uri	string The URI of the retrieved data source.
title	string The title of the retrieved data source.
text	string The content of the retrieved data source.
document_name	string Output only. The full resource name of the referenced Agent Platform Search document. This is used to identify the specific document that was retrieved. The format is projects/{project}/locations/{location}/collections/{collection}/dataStores/{data_store}/branches/{branch}/documents/{document}.

Web

A Web chunk is a piece of evidence that comes from a web page. It contains the URI of the web page, the title of the page, and the domain of the page. This is used to provide the user with a link to the source of the information.

Fields
uri	string The URI of the web page that contains the evidence.
title	string The title of the web page that contains the evidence.
domain	string The domain of the web page that contains the evidence. This can be used to filter out low-quality sources.

GroundingMetadata

Information about the sources that support the content of a response.

When grounding is enabled, the model returns citations for claims in the response. This object contains the retrieved sources.

Fields
web_search_queries[]	string Optional. The web search queries that were used to generate the content. This field is populated only when the grounding source is Google Search.
image_search_queries[]	string Optional. The image search queries that were used to generate the content. This field is populated only when the grounding source is Google Search with the Image Search search_type enabled.
retrieval_queries[]	string Optional. The queries that were executed by the retrieval tools. This field is populated only when the grounding source is a retrieval tool, such as Agent Platform Search.
grounding_chunks[]	GroundingChunk A list of supporting references retrieved from the grounding source. This field is populated when the grounding source is Google Search, Agent Platform Search, or Google Maps.
grounding_supports[]	GroundingSupport Optional. A list of grounding supports that connect the generated content to the grounding chunks. This field is populated when the grounding source is Google Search or Agent Platform Search.
source_flagging_uris[]	SourceFlaggingUri Optional. Output only. A list of URIs that can be used to flag a place or review for inappropriate content. This field is populated only when the grounding source is Google Maps.
search_entry_point	SearchEntryPoint Optional. A web search entry point that can be used to display search results. This field is populated only when the grounding source is Google Search.
retrieval_metadata	RetrievalMetadata Optional. Output only. Metadata related to the retrieval grounding source.
google_maps_widget_context_token	string Optional. Output only. A token that can be used to render a Google Maps widget with the contextual data. This field is populated only when the grounding source is Google Maps.

SourceFlaggingUri

A URI that can be used to flag a place or review for inappropriate content. This is populated only when the grounding source is Google Maps.

Fields
source_id	string The ID of the place or review.
flag_content_uri	string The URI that can be used to flag the content.

GroundingSupport

A collection of supporting references for a segment or part of the model's response.

Fields
grounding_chunk_indices[]	int32 A list of indices into the grounding_chunks field of the GroundingMetadata message. These indices specify which grounding chunks support the claim made in the content segment. For example, if this field has the values [1, 3], it means that grounding_chunks[1] and grounding_chunks[3] are the sources for the claim in the content segment.
confidence_scores[]	float The confidence scores for the support references. This list is parallel to the grounding_chunk_indices list. A score is a value between 0.0 and 1.0, with a higher score indicating a higher confidence that the reference supports the claim. For Gemini 2.0 and before, this list has the same size as grounding_chunk_indices. For Gemini 2.5 and later, this list is empty and should be ignored.
rendered_parts[]	int32 Indices into the rendered_parts field of the GroundingMetadata message. These indices specify which rendered parts are associated with this support message.
segment	Segment The content segment that this support message applies to.

HarmCategory

Harm categories that can be detected in user input and model responses.

Enums
HARM_CATEGORY_UNSPECIFIED	Default value. This value is unused.
HARM_CATEGORY_HATE_SPEECH	Content that promotes violence or incites hatred against individuals or groups based on certain attributes.
HARM_CATEGORY_DANGEROUS_CONTENT	Content that promotes, facilitates, or enables dangerous activities.
HARM_CATEGORY_HARASSMENT	Abusive, threatening, or content intended to bully, torment, or ridicule.
HARM_CATEGORY_SEXUALLY_EXPLICIT	Content that contains sexually explicit material.
HARM_CATEGORY_CIVIC_INTEGRITY	Deprecated: Election filter is not longer supported. The harm category is civic integrity. This item is deprecated!
HARM_CATEGORY_IMAGE_HATE	Images that contain hate speech.
HARM_CATEGORY_IMAGE_DANGEROUS_CONTENT	Images that contain dangerous content.
HARM_CATEGORY_IMAGE_HARASSMENT	Images that contain harassment.
HARM_CATEGORY_IMAGE_SEXUALLY_EXPLICIT	Images that contain sexually explicit content.
HARM_CATEGORY_JAILBREAK	Prompts designed to bypass safety filters.

HttpElementLocation

Enum of location an HTTP element can be.

Enums
HTTP_IN_UNSPECIFIED
HTTP_IN_QUERY	Element is in the HTTP request query.
HTTP_IN_HEADER	Element is in the HTTP request header.
HTTP_IN_PATH	Element is in the HTTP request path.
HTTP_IN_BODY	Element is in the HTTP request body.
HTTP_IN_COOKIE	Element is in the HTTP request cookie.

HyperparameterTuningJob

Represents a HyperparameterTuningJob. A HyperparameterTuningJob has a Study specification and multiple CustomJobs with identical CustomJob specification.

Fields
name	string Output only. Resource name of the HyperparameterTuningJob.
display_name	string Required. The display name of the HyperparameterTuningJob. The name can be up to 128 characters long and can consist of any UTF-8 characters.
study_spec	StudySpec Required. Study configuration of the HyperparameterTuningJob.
max_trial_count	int32 Required. The desired total number of Trials.
parallel_trial_count	int32 Required. The desired number of Trials to run in parallel.
max_failed_trial_count	int32 The number of failed Trials that need to be seen before failing the HyperparameterTuningJob. If set to 0, Agent Platform decides how many Trials must fail before the whole job fails.
trial_job_spec	CustomJobSpec Required. The spec of a trial job. The same spec applies to the CustomJobs created in all the trials.
trials[]	Trial Output only. Trials of the HyperparameterTuningJob.
state	JobState Output only. The detailed state of the job.
create_time	Timestamp Output only. Time when the HyperparameterTuningJob was created.
start_time	Timestamp Output only. Time when the HyperparameterTuningJob for the first time entered the JOB_STATE_RUNNING state.
end_time	Timestamp Output only. Time when the HyperparameterTuningJob entered any of the following states: JOB_STATE_SUCCEEDED, JOB_STATE_FAILED, JOB_STATE_CANCELLED.
update_time	Timestamp Output only. Time when the HyperparameterTuningJob was most recently updated.
error	Status Output only. Only populated when job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED.
labels	map<string, string> The labels with user-defined metadata to organize HyperparameterTuningJobs. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
encryption_spec	EncryptionSpec Customer-managed encryption key options for a HyperparameterTuningJob. If this is set, then all resources created by the HyperparameterTuningJob will be encrypted with the provided encryption key.
satisfies_pzs	bool Output only. Reserved for future use.
satisfies_pzi	bool Output only. Reserved for future use.

IdMatcher

Matcher for Features of an EntityType by Feature ID.

Fields
ids[]	string Required. The following are accepted as ids: A single-element list containing only *, which selects all Features in the target EntityType, or A list containing only Feature IDs, which selects only Features with those IDs in the target EntityType.

ImageConfig

Configuration for image generation.

This message allows you to control various aspects of image generation, such as the output format, aspect ratio, and whether the model can generate images of people.

Fields
image_output_options	ImageOutputOptions Optional. The image output format for generated images.
aspect_ratio	string Optional. The desired aspect ratio for the generated images. The following aspect ratios are supported: "1:1" "2:3", "3:2" "3:4", "4:3" "4:5", "5:4" "9:16", "16:9" "21:9"
person_generation	PersonGeneration Optional. Controls whether the model can generate people.
image_size	string Optional. Specifies the size of generated images. Supported values are 1K, 2K, 4K. If not specified, the model will use default value 1K.

ImageOutputOptions

The image output format for generated images.

Fields
mime_type	string Optional. The image format that the output should be saved as.
compression_quality	int32 Optional. The compression quality of the output image.

PersonGeneration

Enum for controlling the generation of people in images.

Enums
PERSON_GENERATION_UNSPECIFIED	The default behavior is unspecified. The model will decide whether to generate images of people.
ALLOW_ALL	Allows the model to generate images of people, including adults and children.
ALLOW_ADULT	Allows the model to generate images of adults, but not children.
ALLOW_NONE	Prevents the model from generating images of people.

ImportDataConfig

Describes the location from where we import data into a Dataset, together with the labels that will be applied to the DataItems and the Annotations.

Fields
data_item_labels	map<string, string> Labels that will be applied to newly imported DataItems. If an identical DataItem as one being imported already exists in the Dataset, then these labels will be appended to these of the already existing one, and if labels with identical key is imported before, the old label value will be overwritten. If two DataItems are identical in the same import data operation, the labels will be combined and if key collision happens in this case, one of the values will be picked randomly. Two DataItems are considered identical if their content bytes are identical (e.g. image bytes or pdf bytes). These labels will be overridden by Annotation labels specified inside index file referenced by import_schema_uri, e.g. jsonl file.
annotation_labels	map<string, string> Labels that will be applied to newly imported Annotations. If two Annotations are identical, one of them will be deduped. Two Annotations are considered identical if their payload, payload_schema_uri and all of their labels are the same. These labels will be overridden by Annotation labels specified inside index file referenced by import_schema_uri, e.g. jsonl file.
import_schema_uri	string Required. Points to a YAML file stored on Google Cloud Storage describing the import format. Validation will be done against the schema. The schema is defined as an OpenAPI 3.0.2 Schema Object.
Union field source. The source of the input. source can be only one of the following:
gcs_source	GcsSource The Google Cloud Storage location for the input content.

ImportDataOperationMetadata

Runtime operation information for DatasetService.ImportData.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

ImportDataRequest

Request message for DatasetService.ImportData.

Fields
name	string Required. The name of the Dataset resource. Format: projects/{project}/locations/{location}/datasets/{dataset}
import_configs[]	ImportDataConfig Required. The desired input locations. The contents of all input locations will be imported in one batch.

ImportDataResponse

This type has no fields.

Response message for DatasetService.ImportData.

ImportExtensionOperationMetadata

Details of ExtensionRegistryService.ImportExtension operation.

Fields
generic_metadata	GenericOperationMetadata The common part of the operation metadata.

ImportExtensionRequest

Request message for ExtensionRegistryService.ImportExtension.

Fields
parent	string Required. The resource name of the Location to import the Extension in. Format: projects/{project}/locations/{location}
extension	Extension Required. The Extension to import.

ImportFeatureValuesOperationMetadata

Details of operations that perform import Feature values.

Fields
generic_metadata	GenericOperationMetadata Operation metadata for Featurestore import Feature values.
imported_entity_count	int64 Number of entities that have been imported by the operation.
imported_feature_value_count	int64 Number of Feature values that have been imported by the operation.
source_uris[]	string The source URI from where Feature values are imported.
invalid_row_count	int64 The number of rows in input source that weren't imported due to either * Not having any featureValues. * Having a null entityId. * Having a null timestamp. * Not being parsable (applicable for CSV sources).
timestamp_outside_retention_rows_count	int64 The number rows that weren't ingested due to having timestamps outside the retention boundary.
blocking_operation_ids[]	int64 List of ImportFeatureValues operations running under a single EntityType that are blocking this operation.

ImportFeatureValuesRequest

Request message for FeaturestoreService.ImportFeatureValues.

Fields
entity_type	string Required. The resource name of the EntityType grouping the Features for which values are being imported. Format: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entityType}
entity_id_field	string Source column that holds entity IDs. If not provided, entity IDs are extracted from the column named entity_id.
feature_specs[]	FeatureSpec Required. Specifications defining which Feature values to import from the entity. The request fails if no feature_specs are provided, and having multiple feature_specs for one Feature is not allowed.
disable_online_serving	bool If set, data will not be imported for online serving. This is typically used for backfilling, where Feature generation timestamps are not in the timestamp range needed for online serving.
worker_count	int32 Specifies the number of workers that are used to write data to the Featurestore. Consider the online serving capacity that you require to achieve the desired import throughput without interfering with online serving. The value must be positive, and less than or equal to 100. If not set, defaults to using 1 worker. The low count ensures minimal impact on online serving performance.
disable_ingestion_analysis	bool If true, API doesn't start ingestion analysis pipeline.
Union field source. Details about the source data, including the location of the storage and the format. source can be only one of the following:
avro_source	AvroSource
bigquery_source	BigQuerySource
csv_source	CsvSource
Union field feature_time_source. Source of Feature timestamp for all Feature values of each entity. Timestamps must be millisecond-aligned. feature_time_source can be only one of the following:
feature_time_field	string Source column that holds the Feature timestamp for all Feature values in each entity.
feature_time	Timestamp Single Feature timestamp for all entities being imported. The timestamp must not have higher than millisecond precision.

FeatureSpec

Defines the Feature value(s) to import.

Fields
id	string Required. ID of the Feature to import values of. This Feature must exist in the target EntityType, or the request will fail.
source_field	string Source column to get the Feature values from. If not set, uses the column with the same name as the Feature ID.

ImportFeatureValuesResponse

Response message for FeaturestoreService.ImportFeatureValues.

Fields
imported_entity_count	int64 Number of entities that have been imported by the operation.
imported_feature_value_count	int64 Number of Feature values that have been imported by the operation.
invalid_row_count	int64 The number of rows in input source that weren't imported due to either * Not having any featureValues. * Having a null entityId. * Having a null timestamp. * Not being parsable (applicable for CSV sources).
timestamp_outside_retention_rows_count	int64 The number rows that weren't ingested due to having feature timestamps outside the retention boundary.

ImportIndexOperationMetadata

Runtime operation information for IndexService.ImportIndex.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.

ImportIndexRequest

Request message for IndexService.ImportIndex.

Fields
name	string Required. The name of the Index resource to import data to. Format: projects/{project}/locations/{location}/indexes/{index}
is_complete_overwrite	bool Optional. If true, completely replace existing index data. Must be true for streaming update indexes.
config	ConnectorConfig Required. Configuration for importing data from an external source.

ConnectorConfig

Configuration for importing data from an external source.

Fields
Union field source. The source of the data to import. source can be only one of the following:
big_query_source_config	BigQuerySourceConfig Configuration for importing data from a BigQuery table.

BigQuerySourceConfig

Configuration for importing data from a BigQuery table.

Fields
table_path	string Required. The path to the BigQuery table containing the index data, in the format of bq://<project_id>.<dataset_id>.<table>.
datapoint_field_mapping	DatapointFieldMapping Required. Mapping of datapoint fields to BigQuery column names.

DatapointFieldMapping

Mapping of datapoint fields to column names for columnar data sources.

Fields
id_column	string Required. The column with unique identifiers for each data point.
embedding_column	string Required. The column with the vector embeddings for each data point.
restricts[]	Restrict Optional. List of restricts for string values.
numeric_restricts[]	NumericRestrict Optional. List of restricts for numeric values.
metadata_columns[]	string Optional. List of columns containing metadata to be included in the index.

NumericRestrict

Restrictions on numeric values.

Fields
namespace	string Required. The namespace of the restrict.
value_column	string Optional. The column containing the numeric value.
value_type	ValueType Required. Numeric type of the restrict. Must be consistent for all datapoints within the namespace.

ValueType

The type of numeric value for the restrict.

Enums
VALUE_TYPE_UNSPECIFIED	Should not be used.
INT	Represents 64 bit integer.
FLOAT	Represents 32 bit float.
DOUBLE	Represents 64 bit float.

Restrict

Restrictions on string values.

Fields
namespace	string Required. The namespace of the restrict in the index.
allow_column[]	string Optional. The columns containing the allow values.
deny_column[]	string Optional. The columns containing the deny values.

ImportModelEvaluationRequest

Request message for ModelService.ImportModelEvaluation

Fields
parent	string Required. The name of the parent model resource. Format: projects/{project}/locations/{location}/models/{model}
model_evaluation	ModelEvaluation Required. Model evaluation resource to be imported.

ImportRagFilesConfig

Config for importing RagFiles.

Fields
rag_file_chunking_config (deprecated)	RagFileChunkingConfig This item is deprecated! Specifies the size and overlap of chunks after importing RagFiles.
rag_file_transformation_config	RagFileTransformationConfig Specifies the transformation config for RagFiles.
rag_file_parsing_config	RagFileParsingConfig Optional. Specifies the parsing config for RagFiles. RAG will use the default parser if this field is not set.
rag_file_metadata_config (deprecated)	RagFileMetadataConfig This item is deprecated! Specifies the metadata config for RagFiles. Including paths for metadata schema and metadata. Deprecated: Not in use.
max_embedding_requests_per_min	int32 Optional. The max number of queries per minute that this job is allowed to make to the embedding model specified on the corpus. This value is specific to this job and not shared across other import jobs. Consult the Quotas page on the project to set an appropriate value here. If unspecified, a default value of 1,000 QPM would be used.
global_max_embedding_requests_per_min	int32 Optional. The max number of queries per minute that the indexing pipeline job is allowed to make to the embedding model specified in the project. Please follow the quota usage guideline of the embedding model you use to set the value properly.If this value is not specified, max_embedding_requests_per_min will be used by indexing pipeline job as the global limit.
rebuild_ann_index	bool Rebuilds the ANN index to optimize for recall on the imported data. Only applicable for RagCorpora running on RagManagedDb with retrieval_strategy set to ANN. The rebuild will be performed using the existing ANN config set on the RagCorpus. To change the ANN config, please use the UpdateRagCorpus API. Default is false, i.e., index is not rebuilt.
Union field import_source. The source of the import. import_source can be only one of the following:
gcs_source	GcsSource Google Cloud Storage location. Supports importing individual files as well as entire Google Cloud Storage directories. Sample formats: - gs://bucket_name/my_directory/object_name/my_file.txt - gs://bucket_name/my_directory
google_drive_source	GoogleDriveSource Google Drive location. Supports importing individual files as well as Google Drive folders.
slack_source	SlackSource Slack channels with their corresponding access tokens.
jira_source	JiraSource Jira queries with their corresponding authentication.
share_point_sources	SharePointSources SharePoint sources.
Union field partial_failure_sink. Optional. If provided, all partial failures are written to the sink. Deprecated. Prefer to use the import_result_sink. partial_failure_sink can be only one of the following:
partial_failure_gcs_sink (deprecated)	GcsDestination This item is deprecated! The Cloud Storage path to write partial failures to. Deprecated. Prefer to use import_result_gcs_sink.
partial_failure_bigquery_sink (deprecated)	BigQueryDestination This item is deprecated! The BigQuery destination to write partial failures to. It should be a bigquery table resource name (e.g. "bq://projectId.bqDatasetId.bqTableId"). The dataset must exist. If the table does not exist, it will be created with the expected schema. If the table exists, the schema will be validated and data will be added to this existing table. Deprecated. Prefer to use import_result_bq_sink.
Union field import_result_sink. Optional. If provided, all successfully imported files and all partial failures are written to the sink. import_result_sink can be only one of the following:
import_result_gcs_sink	GcsDestination The Cloud Storage path to write import result to.
import_result_bigquery_sink	BigQueryDestination The BigQuery destination to write import result to. It should be a bigquery table resource name (e.g. "bq://projectId.bqDatasetId.bqTableId"). The dataset must exist. If the table does not exist, it will be created with the expected schema. If the table exists, the schema will be validated and data will be added to this existing table.

ImportRagFilesOperationMetadata

Runtime operation information for VertexRagDataService.ImportRagFiles.

Fields
generic_metadata	GenericOperationMetadata The operation generic information.
rag_corpus_id	int64 The resource ID of RagCorpus that this operation is executed on.
import_rag_files_config	ImportRagFilesConfig Output only. The config that was passed in the ImportRagFilesRequest.
progress_percentage	int32 The progress percentage of the operation. Value is in the range [0, 100]. This percentage is calculated as follows: progress_percentage = 100 * (successes + failures + skips) / total

ImportRagFilesRequest

Request message for VertexRagDataService.ImportRagFiles.

Fields
parent	string Required. The name of the RagCorpus resource into which to import files. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}
import_rag_files_config	ImportRagFilesConfig Required. The config for the RagFiles to be synced and imported into the RagCorpus. VertexRagDataService.ImportRagFiles.

ImportRagFilesResponse

Response message for VertexRagDataService.ImportRagFiles.

Fields
imported_rag_files_count	int64 The number of RagFiles that had been imported into the RagCorpus.
failed_rag_files_count	int64 The number of RagFiles that had failed while importing into the RagCorpus.
skipped_rag_files_count	int64 The number of RagFiles that was skipped while importing into the RagCorpus.
Union field partial_failure_sink. The location into which the partial failures were written. partial_failure_sink can be only one of the following:
partial_failures_gcs_path	string The Google Cloud Storage path into which the partial failures were written.
partial_failures_bigquery_table	string The BigQuery table into which the partial failures were written.

Index

A representation of a collection of database items organized in a way that allows for approximate nearest neighbor (a.k.a ANN) algorithms search.

Fields
name	string Output only. The resource name of the Index.
display_name	string Required. The display name of the Index. The name can be up to 128 characters long and can consist of any UTF-8 characters.
description	string The description of the Index.
metadata_schema_uri	string Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Index, that is specific to it. Unset if the Index does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
metadata	Value An additional information about the Index; the schema of the metadata can be found in metadata_schema.
deployed_indexes[]	DeployedIndexRef Output only. The pointers to DeployedIndexes created from this Index. An Index can be only deleted if all its DeployedIndexes had been undeployed first.
etag	string Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map<string, string> The labels with user-defined metadata to organize your Indexes. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
create_time	Timestamp Output only. Timestamp when this Index was created.
update_time	Timestamp Output only. Timestamp when this Index was most recently updated. This also includes any update to the contents of the Index. Note that Operations working on this Index may have their Operations.metadata.generic_metadata.update_time a little after the value of this timestamp, yet that does not mean their results are not already reflected in the Index. Result of any successfully completed Operation on the Index is reflected in it.
index_stats	IndexStats Output only. Stats of the index resource.
index_update_method	IndexUpdateMethod Immutable. The update method to use with this Index. If not set, BATCH_UPDATE will be used by default.
encryption_spec	EncryptionSpec Immutable. Customer-managed encryption key spec for an Index. If set, this Index and all sub-resources of this Index will be secured by this key.
satisfies_pzs	bool Output only. Reserved for future use.
satisfies_pzi	bool Output only. Reserved for future use.

IndexUpdateMethod

The update method of an Index.

Enums
INDEX_UPDATE_METHOD_UNSPECIFIED	Should not be used.
BATCH_UPDATE	BatchUpdate: user can call UpdateIndex with files on Cloud Storage of Datapoints to update.
STREAM_UPDATE	StreamUpdate: user can call UpsertDatapoints/DeleteDatapoints to update the Index and the updates will be applied in corresponding DeployedIndexes in nearly real-time.

IndexDatapoint

A datapoint of Index.

Fields
datapoint_id	string Required. Unique identifier of the datapoint.
feature_vector[]	float Required. Feature embedding vector for dense index. An array of numbers with the length of [NearestNeighborSearchConfig.dimensions].
sparse_embedding	SparseEmbedding Optional. Feature embedding vector for sparse index.
restricts[]	Restriction Optional. List of Restrict of the datapoint, used to perform "restricted searches" where boolean rule are used to filter the subset of the database eligible for matching. This uses categorical tokens. See: https://cloud.google.com/vertex-ai/docs/matching-engine/filtering
numeric_restricts[]	NumericRestriction Optional. List of Restrict of the datapoint, used to perform "restricted searches" where boolean rule are used to filter the subset of the database eligible for matching. This uses numeric comparisons.
crowding_tag	CrowdingTag Optional. CrowdingTag of the datapoint, the number of neighbors to return in each crowding can be configured during query.
embedding_metadata	Struct Optional. The key-value map of additional metadata for the datapoint.

CrowdingTag

Crowding tag is a constraint on a neighbor list produced by nearest neighbor search requiring that no more than some value k' of the k neighbors returned have the same value of crowding_attribute.

Fields
crowding_attribute	string The attribute value used for crowding. The maximum number of neighbors to return per crowding attribute value (per_crowding_attribute_num_neighbors) is configured per-query. This field is ignored if per_crowding_attribute_num_neighbors is larger than the total number of neighbors to return for a given query.

NumericRestriction

This field allows restricts to be based on numeric comparisons rather than categorical tokens.

Fields
namespace	string The namespace of this restriction. e.g.: cost.
op	Operator This MUST be specified for queries and must NOT be specified for datapoints.
Union field Value. The type of Value must be consistent for all datapoints with a given namespace name. This is verified at runtime. Value can be only one of the following:
value_int	int64 Represents 64 bit integer.
value_float	float Represents 32 bit float.
value_double	double Represents 64 bit float.

Operator

Which comparison operator to use. Should be specified for queries only; specifying this for a datapoint is an error.

Datapoints for which Operator is true relative to the query's Value field will be allowlisted.

Enums
OPERATOR_UNSPECIFIED	Default value of the enum.
LESS	Datapoints are eligible iff their value is < the query's.
LESS_EQUAL	Datapoints are eligible iff their value is <= the query's.
EQUAL	Datapoints are eligible iff their value is == the query's.
GREATER_EQUAL	Datapoints are eligible iff their value is >= the query's.
GREATER	Datapoints are eligible iff their value is > the query's.
NOT_EQUAL	Datapoints are eligible iff their value is != the query's.

Restriction

Restriction of a datapoint which describe its attributes(tokens) from each of several attribute categories(namespaces).

Fields
namespace	string The namespace of this restriction. e.g.: color.
allow_list[]	string The attributes to allow in this namespace. e.g.: 'red'
deny_list[]	string The attributes to deny in this namespace. e.g.: 'blue'

SparseEmbedding

Feature embedding vector for sparse index. An array of numbers whose values are located in the specified dimensions.

Fields
values[]	float Required. The list of embedding values of the sparse vector.
dimensions[]	int64 Required. The list of indexes for the embedding values of the sparse vector.

IndexEndpoint

Indexes are deployed into it. An IndexEndpoint can have multiple DeployedIndexes.

Fields
name	string Output only. The resource name of the IndexEndpoint.
display_name	string Required. The display name of the IndexEndpoint. The name can be up to 128 characters long and can consist of any UTF-8 characters.
description	string The description of the IndexEndpoint.
deployed_indexes[]	DeployedIndex Output only. The indexes deployed in this endpoint.
etag	string Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
labels	map<string, string> The labels with user-defined metadata to organize your IndexEndpoints. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.
create_time	Timestamp Output only. Timestamp when this IndexEndpoint was created.
update_time	Timestamp Output only. Timestamp when this IndexEndpoint was last updated. This timestamp is not updated when the endpoint's DeployedIndexes are updated, e.g. due to updates of the original Indexes they are the deployments of.
network	string Optional. The full name of the Google Compute Engine network to which the IndexEndpoint should be peered. Private services access must already be configured for the network. If left unspecified, the Endpoint is not peered with any network. network and private_service_connect_config are mutually exclusive. Format: projects/{project}/global/networks/{network}. Where {project} is a project number, as in '12345', and {network} is network name.
enable_private_service_connect (deprecated)	bool This item is deprecated! Optional. Deprecated: If true, expose the IndexEndpoint via private service connect. Only one of the fields, network or enable_private_service_connect, can be set.
private_service_connect_config	PrivateServiceConnectConfig Optional. Configuration for private service connect. network and private_service_connect_config are mutually exclusive.
public_endpoint_enabled	bool Optional. If true, the deployed index will be accessible through public endpoint.
public_endpoint_domain_name	string Output only. If public_endpoint_enabled is true, this field will be populated with the domain name to use for this index endpoint.
encryption_spec	EncryptionSpec Immutable. Customer-managed encryption key spec for an IndexEndpoint. If set, this IndexEndpoint and all sub-resources of this IndexEndpoint will be secured by this key.
satisfies_pzs	bool Output only. Reserved for future use.
satisfies_pzi	bool Output only. Reserved for future use.

IndexPrivateEndpoints

IndexPrivateEndpoints proto is used to provide paths for users to send requests via private endpoints (e.g. private service access, private service connect). To send request via private service access, use match_grpc_address. To send request via private service connect, use service_attachment.

Fields
match_grpc_address	string Output only. The ip address used to send match gRPC requests.
service_attachment	string Output only. The name of the service attachment resource. Populated if private service connect is enabled.
psc_automated_endpoints[]	PscAutomatedEndpoints Output only. PscAutomatedEndpoints is populated if private service connect is enabled if PscAutomatedConfig is set.

IndexStats

Stats of the Index.

Fields
vectors_count	int64 Output only. The number of dense vectors in the Index.
sparse_vectors_count	int64 Output only. The number of sparse vectors in the Index.
shards_count	int32 Output only. The number of shards in the Index.

IngestEventsMetadata

Metadata for MemoryBankService.IngestEvents.

Fields
generic_metadata	GenericOperationMetadata Output only. The common part of the operation metadata.
stream_id	string Output only. The stream ID used for this ingestion.

IngestEventsRequest

Request message for MemoryBankService.IngestEvents.

Fields
parent	string Required. The resource name of the ReasoningEngine to ingest events to. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}
stream_id	string Optional. The ID of the stream to ingest events into. If not provided, a new one will be created.
generation_trigger_config	MemoryGenerationTriggerConfig Optional. Configuration for triggering memory generation from this ingestion. If not set, then the stream will be force flushed immediately.
scope	map<string, string> Required. The scope of the memories that should be generated from the stream. Memories will be consolidated across memories with the same scope. Scope values cannot contain the wildcard character '*'.
force_flush	bool Optional. Forces a flush of all pending events in the stream and triggers memory generation immediately bypassing any conditions configured in the generation_trigger_config.
Union field source. Source of the events to ingest. source can be only one of the following:
direct_contents_source	IngestionDirectContentsSource Ingest events directly from the request.

IngestEventsResponse

Response message for MemoryBankService.IngestEvents.

Fields
generation_operations[]	string The resource name of the memory generation operation, if triggered.

IngestionDirectContentsSource

Ingest events directly from the request.

Fields
events[]	Event Required. The events to ingest.

Event

A single event to ingest.

Fields
content	Content Required. The content of the event.
event_id	string Optional. A unique identifier for the event. If an event with the same event_id is ingested multiple times, it will be de-duplicated.
event_time	Timestamp Optional. The time at which the event occurred. If provided, this timestamp will be used for ordering events within a stream. If not provided, the server-side ingestion time will be used.

InputDataConfig

Specifies Agent Platform owned input data to be used for training, and possibly evaluating, the Model.

Fields
dataset_id	string Required. The ID of the Dataset in the same Project and Location which data will be used to train the Model. The Dataset must use schema compatible with Model being trained, and what is compatible should be described in the used TrainingPipeline's training_task_definition. For tabular Datasets, all their data is exported to training, to pick and choose from.
annotations_filter	string Applicable only to Datasets that have DataItems and Annotations. A filter on Annotations of the Dataset. Only Annotations that both match this filter and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on (for the auto-assigned that role is decided by Agent Platform). A filter with same syntax as the one used in ListAnnotations may be used, but note here it filters across all Annotations of the Dataset, and not just within a single DataItem.
annotation_schema_uri	string Applicable only to custom training with Datasets that have DataItems and Annotations. Cloud Storage URI that points to a YAML file describing the annotation schema. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/ , note that the chosen schema must be consistent with metadata of the Dataset specified by dataset_id. Only Annotations that both match this schema and belong to DataItems not ignored by the split method are used in respectively training, validation or test role, depending on the role of the DataItem they are on. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both annotations_filter and annotation_schema_uri.
saved_query_id	string Only applicable to Datasets that have SavedQueries. The ID of a SavedQuery (annotation set) under the Dataset specified by dataset_id used for filtering Annotations for training. Only Annotations that are associated with this SavedQuery are used in respectively training. When used in conjunction with annotations_filter, the Annotations used for training are filtered by both saved_query_id and annotations_filter. Only one of saved_query_id and annotation_schema_uri should be specified as both of them represent the same thing: problem type.
persist_ml_use_assignment	bool Whether to persist the ML use assignment to data item system labels.
Union field split. The instructions how the input data should be split between the training, validation and test sets. If no split type is provided, the fraction_split is used by default. split can be only one of the following:
fraction_split	FractionSplit Split based on fractions defining the size of each set.
filter_split	FilterSplit Split based on the provided filters for each set.
predefined_split	PredefinedSplit Supported only for tabular Datasets. Split based on a predefined key.
timestamp_split	TimestampSplit Supported only for tabular Datasets. Split based on the timestamp of the input data pieces.
stratified_split	StratifiedSplit Supported only for tabular Datasets. Split based on the distribution of the specified column.
Union field destination. Only applicable to Custom and Hyperparameter Tuning TrainingPipelines. The destination of the training data to be written to. Supported destination file formats: * For non-tabular data: "jsonl". * For tabular data: "csv" and "bigquery". The following Agent Platform environment variables are passed to containers or python modules of the training task when this field is set: AIP_DATA_FORMAT : Exported data format. AIP_TRAINING_DATA_URI : Sharded exported training data uris. AIP_VALIDATION_DATA_URI : Sharded exported validation data uris. AIP_TEST_DATA_URI : Sharded exported test data uris. destination can be only one of the following:
gcs_destination	GcsDestination The Cloud Storage location where the training data is to be written to. In the given directory a new directory is created with name: dataset-<dataset-id>-<annotation-type>-<timestamp-of-training-call> where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All training input data is written into that directory. The Agent Platform environment variables representing Cloud Storage data URIs are represented in the Cloud Storage wildcard format to support sharded data. e.g.: "gs://.../training-*.jsonl" AIP_DATA_FORMAT = "jsonl" for non-tabular data, "csv" for tabular data AIP_TRAINING_DATA_URI = "gcs_destination/dataset---/training-*.${AIP_DATA_FORMAT}" AIP_VALIDATION_DATA_URI = "gcs_destination/dataset---/validation-*.${AIP_DATA_FORMAT}" AIP_TEST_DATA_URI = "gcs_destination/dataset---/test-*.${AIP_DATA_FORMAT}"
bigquery_destination	BigQueryDestination Only applicable to custom training with tabular Dataset with BigQuery source. The BigQuery project location where the training data is to be written to. In the given project a new dataset is created with name dataset_<dataset-id>_<annotation-type>_<timestamp-of-training-call> where timestamp is in YYYY_MM_DDThh_mm_ss_sssZ format. All training input data is written into that dataset. In the dataset three tables are created, training, validation and test. AIP_DATA_FORMAT = "bigquery". AIP_TRAINING_DATA_URI = "bigquery_destination.dataset___.training" AIP_VALIDATION_DATA_URI = "bigquery_destination.dataset___.validation" AIP_TEST_DATA_URI = "bigquery_destination.dataset___.test"

Int64Array

A list of int64 values.

Fields
values[]	int64 A list of int64 values.

IntegratedGradientsAttribution

An attribution method that computes the Aumann-Shapley value taking advantage of the model's fully differentiable structure. Refer to this paper for more details: https://arxiv.org/abs/1703.01365

Fields
step_count	int32 Required. The number of steps for approximating the path integral. A good value to start is 50 and gradually increase until the sum to diff property is within the desired error range. Valid range of its value is [1, 100], inclusively.
smooth_grad_config	SmoothGradConfig Config for SmoothGrad approximation of gradients. When enabled, the gradients are approximated by averaging the gradients from noisy samples in the vicinity of the inputs. Adding noise can help improve the computed gradients. Refer to this paper for more details: https://arxiv.org/pdf/1706.03825.pdf
blur_baseline_config	BlurBaselineConfig Config for IG with blur baseline. When enabled, a linear path from the maximally blurred image to the input image is created. Using a blurred baseline instead of zero (black image) is motivated by the BlurIG approach explained here: https://arxiv.org/abs/2004.03383

InvokeReasoningEngineRequest

Request message for ReasoningEngineExecutionService.InvokeReasoningEngine.

Fields
name	string Required. The name of the ReasoningEngine resource to use. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine} or projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}/runtimeRevisions/{runtime_revision}
http_body	HttpBody Optional. The invoke method input. Supports HTTP headers and arbitrary data payload.

JiraSource

The Jira source for the ImportRagFilesRequest.

Fields
jira_queries[]	JiraQueries Required. The Jira queries.

JiraQueries

JiraQueries contains the Jira queries and corresponding authentication.

Fields
projects[]	string A list of Jira projects to import in their entirety.
custom_queries[]	string A list of custom Jira queries to import. For information about JQL (Jira Query Language), see https://support.atlassian.com/jira-service-management-cloud/docs/use-advanced-search-with-jira-query-language-jql/
email	string Required. The Jira email address.
server_uri	string Required. The Jira server URI.
api_key_config	ApiKeyConfig Required. The SecretManager secret version resource name (e.g. projects/{project}/secrets/{secret}/versions/{version}) storing the Jira API key. See Manage API tokens for your Atlassian account.

JobState

Describes the state of a job.

Enums
JOB_STATE_UNSPECIFIED	The job state is unspecified.
JOB_STATE_QUEUED	The job has been just created or resumed and processing has not yet begun.
JOB_STATE_PENDING	The service is preparing to run the job.
JOB_STATE_RUNNING	The job is in progress.
JOB_STATE_SUCCEEDED	The job completed successfully.
JOB_STATE_FAILED	The job failed.
JOB_STATE_CANCELLING	The job is being cancelled. From this state the job may only go to either JOB_STATE_SUCCEEDED, JOB_STATE_FAILED or JOB_STATE_CANCELLED.
JOB_STATE_CANCELLED	The job has been cancelled.
JOB_STATE_PAUSED	The job has been stopped, and can be resumed.
JOB_STATE_EXPIRED	The job has expired.
JOB_STATE_UPDATING	The job is being updated. Only jobs in the RUNNING state can be updated. After updating, the job goes back to the RUNNING state.
JOB_STATE_PARTIALLY_SUCCEEDED	The job is partially succeeded, some results may be missing due to errors.

KeepAliveProbe

Represents the configuration for keep-alive probe. Contains configuration on a specified endpoint that a deployment host should use to keep the container alive based on the probe settings.

Fields
http_get	HttpGet Optional. Specifies the HTTP GET configuration for the probe.
max_seconds	int32 Optional. Specifies the maximum duration (in seconds) to keep the instance alive via this probe. Can be a maximum of 3600 seconds (1 hour).

HttpGet

Specifies the HTTP GET configuration for the probe.

Fields
path	string Required. Specifies the path of the HTTP GET request (e.g., "/is_busy").
port	int32 Optional. Specifies the port number on the container to which the request is sent.

LLMBasedMetricSpec

Specification for an LLM based metric.

Fields
result_parser_config	EvaluationParserConfig Optional. The parser config for the metric result.
Union field rubrics_source. Source of the rubrics to be used for evaluation. rubrics_source can be only one of the following:
rubric_group_key	string Use a pre-defined group of rubrics associated with the input. Refers to a key in the rubric_groups map of EvaluationInstance.
rubric_generation_spec	RubricGenerationSpec Dynamically generate rubrics using this specification.
predefined_rubric_generation_spec	PredefinedMetricSpec Dynamically generate rubrics using a predefined spec.
metric_prompt_template	string Required. Template for the prompt sent to the judge model.
system_instruction	string Optional. System instructions for the judge model.
judge_autorater_config	AutoraterConfig Optional. Optional configuration for the judge LLM (Autorater).
additional_config	Struct Optional. Optional additional configuration for the metric.

LargeModelReference

Contains information about the Large Model.

Fields
name	string Required. The unique name of the large Foundation or pre-built model. Like "chat-bison", "text-bison". Or model name with version ID, like "chat-bison@001", "text-bison@005", etc.

LineageSubgraph

A subgraph of the overall lineage graph. Event edges connect Artifact and Execution nodes.

Fields
artifacts[]	Artifact The Artifact nodes in the subgraph.
executions[]	Execution The Execution nodes in the subgraph.
events[]	Event The Event edges between Artifacts and Executions in the subgraph.

ListAgentsRequest

Request message for AgentService.ListAgents.

Fields
parent	string Required. The resource name of the location to list agents from. Format: projects/{project}/locations/{location}.
page_size	int32 Optional. The maximum number of agents to return. The service may return fewer than this value. The maximum page size is 100; values above 100 will be coerced to 100. If unspecified, the default page size is 10.
page_token	string Optional. A page token, received from a previous AgentService.ListAgents call. Provide this to retrieve the subsequent page.
order_by	string Optional. A comma-separated list of fields to order by. Supported fields: created updated Use desc after a field name for descending order. Example: created desc.

ListAgentsResponse

Response message for AgentService.ListAgents.

Fields
agents[]	Agent The agents matching the request.
next_page_token	string A token to retrieve the next page of results. Pass this value as ListAgentsRequest.page_token in a subsequent call.

ListAnnotationsRequest

Request message for DatasetService.ListAnnotations.

Fields
parent	string Required. The resource name of the DataItem to list Annotations from. Format: projects/{project}/locations/{location}/datasets/{dataset}/dataItems/{data_item}
filter	string The standard list filter.
page_size	int32 The standard list page size.
page_token	string The standard list page token.
read_mask	FieldMask Mask specifying which fields to read.
order_by	string A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending.

ListAnnotationsResponse

Response message for DatasetService.ListAnnotations.

Fields
annotations[]	Annotation A list of Annotations that matches the specified filter in the request.
next_page_token	string The standard List next-page token.

ListArtifactsRequest

Request message for MetadataService.ListArtifacts.

Fields
parent	string Required. The MetadataStore whose Artifacts should be listed. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}
page_size	int32 The maximum number of Artifacts to return. The service may return fewer. Must be in range 1-100, inclusive. Defaults to 100.
page_token	string A page token, received from a previous MetadataService.ListArtifacts call. Provide this to retrieve the subsequent page. When paginating, all other provided parameters must match the call that provided the page token. (Otherwise the request will fail with INVALID_ARGUMENT error.)
filter	string Filter specifying the boolean condition for the Artifacts to satisfy in order to be part of the result set. The syntax to define filter query is based on https://google.aip.dev/160. The supported set of filters include the following: Attribute filtering: For example: display_name = "test". Supported fields include: name, display_name, uri, state, schema_title, create_time, and update_time. Time fields, such as create_time and update_time, require values specified in RFC-3339 format. For example: create_time = "2020-11-19T11:30:00-04:00" Metadata field: To filter on metadata fields use traversal operation as follows: metadata.<field_name>.<type_value>. For example: metadata.field_1.number_value = 10.0 In case the field name contains special characters (such as colon), one can embed it inside double quote. For example: metadata."field:1".number_value = 10.0 Context based filtering: To filter Artifacts based on the contexts to which they belong, use the function operator with the full resource name in_context(<context-name>). For example: in_context("projects/<project_number>/locations/<location>/metadataStores/<metadatastore_name>/contexts/<context-id>") Each of the above supported filter types can be combined together using logical operators (AND & OR). Maximum nested expression depth allowed is 5. For example: display_name = "test" AND metadata.field1.bool_value = true.
order_by	string How the list of messages is ordered. Specify the values to order by and an ordering operation. The default sorting order is ascending. To specify descending order for a field, users append a " desc" suffix; for example: "foo desc, bar". Subfields are specified with a . character, such as foo.bar. see https://google.aip.dev/132#ordering for more details.

ListArtifactsResponse

Response message for MetadataService.ListArtifacts.

Fields
artifacts[]	Artifact The Artifacts retrieved from the MetadataStore.
next_page_token	string A token, which can be sent as ListArtifactsRequest.page_token to retrieve the next page. If this field is not populated, there are no subsequent pages.

ListBatchPredictionJobsRequest

Request message for JobService.ListBatchPredictionJobs.

Fields
parent	string Required. The resource name of the Location to list the BatchPredictionJobs from. Format: projects/{project}/locations/{location}
filter	string The standard list filter. Supported fields: display_name supports =, != comparisons, and : wildcard. model_display_name supports =, != comparisons. state supports =, != comparisons. create_time supports =, !=,<, <=,>, >= comparisons. create_time must be in RFC 3339 format. labels supports general map functions that is: labels.key=value - key:value equality `labels.key:* - key existence Some examples of using the filter are: state="JOB_STATE_SUCCEEDED" AND display_name:"my_job_*" state!="JOB_STATE_FAILED" OR display_name="my_job" NOT display_name="my_job" create_time>"2021-05-18T00:00:00Z" labels.keyA=valueA labels.keyB:*
page_size	int32 The standard list page size.
page_token	string The standard list page token. Typically obtained via ListBatchPredictionJobsResponse.next_page_token of the previous JobService.ListBatchPredictionJobs call.
read_mask	FieldMask Mask specifying which fields to read.

ListBatchPredictionJobsResponse

Response message for JobService.ListBatchPredictionJobs

Fields
batch_prediction_jobs[]	BatchPredictionJob List of BatchPredictionJobs in the requested page.
next_page_token	string A token to retrieve the next page of results. Pass to ListBatchPredictionJobsRequest.page_token to obtain that page.

ListCachedContentsRequest

Request to list CachedContents.

Fields
parent	string Required. The parent, which owns this collection of cached contents.
page_size	int32 Optional. The maximum number of cached contents to return. The service may return fewer than this value. If unspecified, some default (under maximum) number of items will be returned. The maximum value is 1000; values above 1000 will be coerced to 1000.
page_token	string Optional. A page token, received from a previous ListCachedContents call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to ListCachedContents must match the call that provided the page token.

ListCachedContentsResponse

Response with a list of CachedContents.

Fields
cached_contents[]	CachedContent List of cached contents.
next_page_token	string A token, which can be sent as page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

ListContextsRequest

Request message for MetadataService.ListContexts

Fields
parent	string Required. The MetadataStore whose Contexts should be listed. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}
page_size	int32 The maximum number of Contexts to return. The service may return fewer. Must be in range 1-100, inclusive. Defaults to 100.
page_token	string A page token, received from a previous MetadataService.ListContexts call. Provide this to retrieve the subsequent page. When paginating, all other provided parameters must match the call that provided the page token. (Otherwise the request will fail with INVALID_ARGUMENT error.)
filter	string Filter specifying the boolean condition for the Contexts to satisfy in order to be part of the result set. The syntax to define filter query is based on https://google.aip.dev/160. Following are the supported set of filters: Attribute filtering: For example: display_name = "test". Supported fields include: name, display_name, schema_title, create_time, and update_time. Time fields, such as create_time and update_time, require values specified in RFC-3339 format. For example: create_time = "2020-11-19T11:30:00-04:00". Metadata field: To filter on metadata fields use traversal operation as follows: metadata.<field_name>.<type_value>. For example: metadata.field_1.number_value = 10.0. In case the field name contains special characters (such as colon), one can embed it inside double quote. For example: metadata."field:1".number_value = 10.0 Parent Child filtering: To filter Contexts based on parent-child relationship use the HAS operator as follows: parent_contexts: "projects/<project_number>/locations/<location>/metadataStores/<metadatastore_name>/contexts/<context_id>" child_contexts: "projects/<project_number>/locations/<location>/metadataStores/<metadatastore_name>/contexts/<context_id>" Each of the above supported filters can be combined together using logical operators (AND & OR). Maximum nested expression depth allowed is 5. For example: display_name = "test" AND metadata.field1.bool_value = true.
order_by	string How the list of messages is ordered. Specify the values to order by and an ordering operation. The default sorting order is ascending. To specify descending order for a field, users append a " desc" suffix; for example: "foo desc, bar". Subfields are specified with a . character, such as foo.bar. see https://google.aip.dev/132#ordering for more details.

ListContextsResponse

Response message for MetadataService.ListContexts.

Fields
contexts[]	Context The Contexts retrieved from the MetadataStore.
next_page_token	string A token, which can be sent as ListContextsRequest.page_token to retrieve the next page. If this field is not populated, there are no subsequent pages.

ListCustomJobsRequest

Request message for JobService.ListCustomJobs.

Fields
parent	string Required. The resource name of the Location to list the CustomJobs from. Format: projects/{project}/locations/{location}
filter	string The standard list filter. Supported fields: display_name supports =, != comparisons, and : wildcard. state supports =, != comparisons. create_time supports =, !=,<, <=,>, >= comparisons. create_time must be in RFC 3339 format. labels supports general map functions that is: labels.key=value - key:value equality `labels.key:* - key existence Some examples of using the filter are: state="JOB_STATE_SUCCEEDED" AND display_name:"my_job_*" state!="JOB_STATE_FAILED" OR display_name="my_job" NOT display_name="my_job" create_time>"2021-05-18T00:00:00Z" labels.keyA=valueA labels.keyB:*
page_size	int32 The standard list page size.
page_token	string The standard list page token. Typically obtained via ListCustomJobsResponse.next_page_token of the previous JobService.ListCustomJobs call.
read_mask	FieldMask Mask specifying which fields to read.

ListCustomJobsResponse

Response message for JobService.ListCustomJobs

Fields
custom_jobs[]	CustomJob List of CustomJobs in the requested page.
next_page_token	string A token to retrieve the next page of results. Pass to ListCustomJobsRequest.page_token to obtain that page.

ListDataItemsRequest

Request message for DatasetService.ListDataItems.

Fields
parent	string Required. The resource name of the Dataset to list DataItems from. Format: projects/{project}/locations/{location}/datasets/{dataset}
filter	string The standard list filter.
page_size	int32 The standard list page size.
page_token	string The standard list page token.
read_mask	FieldMask Mask specifying which fields to read.
order_by	string A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending.

ListDataItemsResponse

Response message for DatasetService.ListDataItems.

Fields
data_items[]	DataItem A list of DataItems that matches the specified filter in the request.
next_page_token	string The standard List next-page token.

ListDatasetVersionsRequest

Request message for DatasetService.ListDatasetVersions.

Fields
parent	string Required. The resource name of the Dataset to list DatasetVersions from. Format: projects/{project}/locations/{location}/datasets/{dataset}
filter	string Optional. The standard list filter.
page_size	int32 Optional. The standard list page size.
page_token	string Optional. The standard list page token.
read_mask	FieldMask Optional. Mask specifying which fields to read.
order_by	string Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending.

ListDatasetVersionsResponse

Response message for DatasetService.ListDatasetVersions.

Fields
dataset_versions[]	DatasetVersion A list of DatasetVersions that matches the specified filter in the request.
next_page_token	string The standard List next-page token.

ListDatasetsRequest

Request message for DatasetService.ListDatasets.

Fields
parent	string Required. The name of the Dataset's parent resource. Format: projects/{project}/locations/{location}
filter	string An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. display_name: supports = and != metadata_schema_uri: supports = and != labels supports general map functions that is: labels.key=value - key:value equality `labels.key:* or labels:key - key existence A key including a space must be quoted. labels."a key". Some examples: displayName="myDisplayName" labels.myKey="myValue"
page_size	int32 The standard list page size.
page_token	string The standard list page token.
read_mask	FieldMask Mask specifying which fields to read.
order_by	string A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: display_name create_time update_time

ListDatasetsResponse

Response message for DatasetService.ListDatasets.

Fields
datasets[]	Dataset A list of Datasets that matches the specified filter in the request.
next_page_token	string The standard List next-page token.

ListDeploymentResourcePoolsRequest

Request message for ListDeploymentResourcePools method.

Fields
parent	string Required. The parent Location which owns this collection of DeploymentResourcePools. Format: projects/{project}/locations/{location}
page_size	int32 The maximum number of DeploymentResourcePools to return. The service may return fewer than this value.
page_token	string A page token, received from a previous ListDeploymentResourcePools call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to ListDeploymentResourcePools must match the call that provided the page token.

ListDeploymentResourcePoolsResponse

Response message for ListDeploymentResourcePools method.

Fields
deployment_resource_pools[]	DeploymentResourcePool The DeploymentResourcePools from the specified location.
next_page_token	string A token, which can be sent as page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

ListEndpointsRequest

Request message for EndpointService.ListEndpoints.

Fields
parent	string Required. The resource name of the Location from which to list the Endpoints. Format: projects/{project}/locations/{location}
filter	string Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. endpoint supports = and !=. endpoint represents the Endpoint ID, i.e. the last segment of the Endpoint's resource name. display_name supports = and !=. labels supports general map functions that is: labels.key=value - key:value equality labels.key:* or labels:key - key existence A key including a space must be quoted. labels."a key". base_model_name only supports =. Some examples: endpoint=1 displayName="myDisplayName" labels.myKey="myValue" baseModelName="text-bison"
page_size	int32 Optional. The standard list page size.
page_token	string Optional. The standard list page token. Typically obtained via ListEndpointsResponse.next_page_token of the previous EndpointService.ListEndpoints call.
read_mask	FieldMask Optional. Mask specifying which fields to read.
gdc_zone	string Optional. Configures the Google Distributed Cloud (GDC) environment for online prediction. Only set this field when the Endpoint is to be deployed in a GDC environment.

ListEndpointsResponse

Response message for EndpointService.ListEndpoints.

Fields
endpoints[]	Endpoint List of Endpoints in the requested page.
next_page_token	string A token to retrieve the next page of results. Pass to ListEndpointsRequest.page_token to obtain that page.

ListEntityTypesRequest

Request message for FeaturestoreService.ListEntityTypes.

Fields
parent	string Required. The resource name of the Featurestore to list EntityTypes. Format: projects/{project}/locations/{location}/featurestores/{featurestore}
filter	string Lists the EntityTypes that match the filter expression. The following filters are supported: create_time: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format. update_time: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format. labels: Supports key-value equality as well as key presence. Examples: create_time > \"2020-01-31T15:30:00.000000Z\" OR update_time > \"2020-01-31T15:30:00.000000Z\" --> EntityTypes created or updated after 2020-01-31T15:30:00.000000Z. labels.active = yes AND labels.env = prod --> EntityTypes having both (active: yes) and (env: prod) labels. labels.env: * --> Any EntityType which has a label with 'env' as the key.
page_size	int32 The maximum number of EntityTypes to return. The service may return fewer than this value. If unspecified, at most 1000 EntityTypes will be returned. The maximum value is 1000; any value greater than 1000 will be coerced to 1000.
page_token	string A page token, received from a previous FeaturestoreService.ListEntityTypes call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to FeaturestoreService.ListEntityTypes must match the call that provided the page token.
order_by	string A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: entity_type_id create_time update_time
read_mask	FieldMask Mask specifying which fields to read.

ListEntityTypesResponse

Response message for FeaturestoreService.ListEntityTypes.

Fields
entity_types[]	EntityType The EntityTypes matching the request.
next_page_token	string A token, which can be sent as ListEntityTypesRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

ListEventsRequest

Request message for SessionService.ListEvents.

Fields
parent	string Required. The resource name of the session to list events from. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}/sessions/{session}
page_size	int32 Optional. The maximum number of events to return. The service may return fewer than this value. If unspecified, at most 100 events will be returned. These events are ordered by timestamp in ascending order.
page_token	string Optional. The next_page_token value returned from a previous list SessionService.ListEvents call.
filter	string Optional. The standard list filter. Supported fields: * timestamp range (i.e. timestamp>="2025-01-31T11:30:00-04:00" where the timestamp is in RFC 3339 format) More detail in AIP-160.
order_by	string Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: * timestamp Example: timestamp desc.

ListEventsResponse

Response message for SessionService.ListEvents.

Fields
session_events[]	SessionEvent A list of events matching the request. Ordered by timestamp in ascending order.
next_page_token	string A token, which can be sent as ListEventsRequest.page_token to retrieve the next page. Absence of this field indicates there are no subsequent pages.

ListExampleStoresRequest

Request message for ExampleStoreService.ListExampleStores.

Fields
parent	string Required. The resource name of the Location to list the ExampleStores from. Format: projects/{project}/locations/{location}
filter	string Optional. The standard list filter. More detail in AIP-160.
page_size	int32 Optional. The standard list page size.
page_token	string Optional. The standard list page token.

ListExampleStoresResponse

Response message for ExampleStoreService.ListExampleStores.

Fields
example_stores[]	ExampleStore List of ExampleStore in the requested page.
next_page_token	string A token to retrieve the next page of results. Pass to ListExampleStoresRequest.page_token to obtain that page.

ListExecutionsRequest

Request message for MetadataService.ListExecutions.

Fields
parent	string Required. The MetadataStore whose Executions should be listed. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}
page_size	int32 The maximum number of Executions to return. The service may return fewer. Must be in range 1-100, inclusive. Defaults to 100.
page_token	string A page token, received from a previous MetadataService.ListExecutions call. Provide this to retrieve the subsequent page. When paginating, all other provided parameters must match the call that provided the page token. (Otherwise the request will fail with an INVALID_ARGUMENT error.)
filter	string Filter specifying the boolean condition for the Executions to satisfy in order to be part of the result set. The syntax to define filter query is based on https://google.aip.dev/160. Following are the supported set of filters: Attribute filtering: For example: display_name = "test". Supported fields include: name, display_name, state, schema_title, create_time, and update_time. Time fields, such as create_time and update_time, require values specified in RFC-3339 format. For example: create_time = "2020-11-19T11:30:00-04:00". Metadata field: To filter on metadata fields use traversal operation as follows: metadata.<field_name>.<type_value> For example: metadata.field_1.number_value = 10.0 In case the field name contains special characters (such as colon), one can embed it inside double quote. For example: metadata."field:1".number_value = 10.0 Context based filtering: To filter Executions based on the contexts to which they belong use the function operator with the full resource name: in_context(<context-name>). For example: in_context("projects/<project_number>/locations/<location>/metadataStores/<metadatastore_name>/contexts/<context-id>") Each of the above supported filters can be combined together using logical operators (AND & OR). Maximum nested expression depth allowed is 5. For example: display_name = "test" AND metadata.field1.bool_value = true.
order_by	string How the list of messages is ordered. Specify the values to order by and an ordering operation. The default sorting order is ascending. To specify descending order for a field, users append a " desc" suffix; for example: "foo desc, bar". Subfields are specified with a . character, such as foo.bar. see https://google.aip.dev/132#ordering for more details.

ListExecutionsResponse

Response message for MetadataService.ListExecutions.

Fields
executions[]	Execution The Executions retrieved from the MetadataStore.
next_page_token	string A token, which can be sent as ListExecutionsRequest.page_token to retrieve the next page. If this field is not populated, there are no subsequent pages.

ListExtensionsRequest

Request message for ExtensionRegistryService.ListExtensions.

Fields
parent	string Required. The resource name of the Location to list the Extensions from. Format: projects/{project}/locations/{location}
filter	string Optional. The standard list filter. Supported fields: * display_name * create_time * update_time More detail in AIP-160.
page_size	int32 Optional. The standard list page size.
page_token	string Optional. The standard list page token.
order_by	string Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: * display_name * create_time * update_time Example: display_name, create_time desc.

ListExtensionsResponse

Response message for ExtensionRegistryService.ListExtensions

Fields
extensions[]	Extension List of Extension in the requested page.
next_page_token	string A token to retrieve the next page of results. Pass to ListExtensionsRequest.page_token to obtain that page.

ListFeatureGroupsRequest

Request message for FeatureRegistryService.ListFeatureGroups.

Fields
parent	string Required. The resource name of the Location to list FeatureGroups. Format: projects/{project}/locations/{location}
filter	string Lists the FeatureGroups that match the filter expression. The following fields are supported: create_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format. update_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format. labels: Supports key-value equality and key presence. Examples: create_time > "2020-01-01" OR update_time > "2020-01-01" FeatureGroups created or updated after 2020-01-01. labels.env = "prod" FeatureGroups with label "env" set to "prod".
page_size	int32 The maximum number of FeatureGroups to return. The service may return fewer than this value. If unspecified, at most 100 FeatureGroups will be returned. The maximum value is 100; any value greater than 100 will be coerced to 100.
page_token	string A page token, received from a previous FeatureRegistryService.ListFeatureGroups call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to FeatureRegistryService.ListFeatureGroups must match the call that provided the page token.
order_by	string A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported Fields: create_time update_time

ListFeatureGroupsResponse

Response message for FeatureRegistryService.ListFeatureGroups.

Fields
feature_groups[]	FeatureGroup The FeatureGroups matching the request.
next_page_token	string A token, which can be sent as ListFeatureGroupsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

ListFeatureMonitorJobsRequest

Request message for FeatureRegistryService.ListFeatureMonitorJobs.

Fields
parent	string Required. The resource name of the FeatureMonitor to list FeatureMonitorJobs. Format: projects/{project}/locations/{location}/featureGroups/{feature_group}/featureMonitors/{feature_monitor}
filter	string Optional. Lists the FeatureMonitorJobs that match the filter expression. The following fields are supported: create_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be Examples: create_time > "2020-01-01" FeatureMonitorJobs created after 2020-01-01.
page_size	int32 Optional. The maximum number of FeatureMonitorJobs to return. The service may return fewer than this value. If unspecified, at most 100 FeatureMonitorJobs will be returned. The maximum value is 100; any value greater than 100 will be coerced to 100.
page_token	string Optional. A page token, received from a previous FeatureRegistryService.ListFeatureMonitorJobs call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to FeatureRegistryService.ListFeatureMonitorJobs must match the call that provided the page token.
order_by	string Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported Fields: create_time

ListFeatureMonitorJobsResponse

Response message for FeatureRegistryService.ListFeatureMonitorJobs.

Fields
feature_monitor_jobs[]	FeatureMonitorJob The FeatureMonitorJobs matching the request.
next_page_token	string A token, which can be sent as ListFeatureMonitorJobsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

ListFeatureMonitorsRequest

Request message for FeatureRegistryService.ListFeatureMonitors.

Fields
parent	string Required. The resource name of the FeatureGroup to list FeatureMonitors. Format: projects/{project}/locations/{location}/featureGroups/{featureGroup}
filter	string Optional. Lists the FeatureMonitors that match the filter expression. The following fields are supported: create_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format. update_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format. labels: Supports key-value equality and key presence. Examples: create_time > "2020-01-01" OR update_time > "2020-01-01" FeatureMonitors created or updated after 2020-01-01. labels.env = "prod" FeatureGroups with label "env" set to "prod".
page_size	int32 Optional. The maximum number of FeatureGroups to return. The service may return fewer than this value. If unspecified, at most 100 FeatureMonitors will be returned. The maximum value is 100; any value greater than 100 will be coerced to 100.
page_token	string Optional. A page token, received from a previous FeatureRegistryService.ListFeatureMonitors call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to FeatureRegistryService.ListFeatureMonitors must match the call that provided the page token.
order_by	string Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported Fields: create_time update_time

ListFeatureMonitorsResponse

Response message for FeatureRegistryService.ListFeatureMonitors.

Fields
feature_monitors[]	FeatureMonitor The FeatureMonitors matching the request.
next_page_token	string A token, which can be sent as ListFeatureMonitorsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

ListFeatureOnlineStoresRequest

Request message for FeatureOnlineStoreAdminService.ListFeatureOnlineStores.

Fields
parent	string Required. The resource name of the Location to list FeatureOnlineStores. Format: projects/{project}/locations/{location}
filter	string Lists the FeatureOnlineStores that match the filter expression. The following fields are supported: create_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format. update_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format. labels: Supports key-value equality and key presence. Examples: create_time > "2020-01-01" OR update_time > "2020-01-01" FeatureOnlineStores created or updated after 2020-01-01. labels.env = "prod" FeatureOnlineStores with label "env" set to "prod".
page_size	int32 The maximum number of FeatureOnlineStores to return. The service may return fewer than this value. If unspecified, at most 100 FeatureOnlineStores will be returned. The maximum value is 100; any value greater than 100 will be coerced to 100.
page_token	string A page token, received from a previous FeatureOnlineStoreAdminService.ListFeatureOnlineStores call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to FeatureOnlineStoreAdminService.ListFeatureOnlineStores must match the call that provided the page token.
order_by	string A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported Fields: create_time update_time

ListFeatureOnlineStoresResponse

Response message for FeatureOnlineStoreAdminService.ListFeatureOnlineStores.

Fields
feature_online_stores[]	FeatureOnlineStore The FeatureOnlineStores matching the request.
next_page_token	string A token, which can be sent as ListFeatureOnlineStoresRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

ListFeatureViewSyncsRequest

Request message for FeatureOnlineStoreAdminService.ListFeatureViewSyncs.

Fields
parent	string Required. The resource name of the FeatureView to list FeatureViewSyncs. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}
filter	string Lists the FeatureViewSyncs that match the filter expression. The following filters are supported: create_time: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format. Examples: create_time > \"2020-01-31T15:30:00.000000Z\" --> FeatureViewSyncs created after 2020-01-31T15:30:00.000000Z.
page_size	int32 The maximum number of FeatureViewSyncs to return. The service may return fewer than this value. If unspecified, at most 1000 FeatureViewSyncs will be returned. The maximum value is 1000; any value greater than 1000 will be coerced to 1000.
page_token	string A page token, received from a previous FeatureOnlineStoreAdminService.ListFeatureViewSyncs call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to FeatureOnlineStoreAdminService.ListFeatureViewSyncs must match the call that provided the page token.
order_by	string A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: create_time

ListFeatureViewSyncsResponse

Response message for FeatureOnlineStoreAdminService.ListFeatureViewSyncs.

Fields
feature_view_syncs[]	FeatureViewSync The FeatureViewSyncs matching the request.
next_page_token	string A token, which can be sent as ListFeatureViewSyncsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

ListFeatureViewsRequest

Request message for FeatureOnlineStoreAdminService.ListFeatureViews.

Fields
parent	string Required. The resource name of the FeatureOnlineStore to list FeatureViews. Format: projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}
filter	string Lists the FeatureViews that match the filter expression. The following filters are supported: create_time: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format. update_time: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format. labels: Supports key-value equality as well as key presence. Examples: create_time > \"2020-01-31T15:30:00.000000Z\" OR update_time > \"2020-01-31T15:30:00.000000Z\" --> FeatureViews created or updated after 2020-01-31T15:30:00.000000Z. labels.active = yes AND labels.env = prod --> FeatureViews having both (active: yes) and (env: prod) labels. labels.env: * --> Any FeatureView which has a label with 'env' as the key.
page_size	int32 The maximum number of FeatureViews to return. The service may return fewer than this value. If unspecified, at most 1000 FeatureViews will be returned. The maximum value is 1000; any value greater than 1000 will be coerced to 1000.
page_token	string A page token, received from a previous FeatureOnlineStoreAdminService.ListFeatureViews call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to FeatureOnlineStoreAdminService.ListFeatureViews must match the call that provided the page token.
order_by	string A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: feature_view_id create_time update_time

ListFeatureViewsResponse

Response message for FeatureOnlineStoreAdminService.ListFeatureViews.

Fields
feature_views[]	FeatureView The FeatureViews matching the request.
next_page_token	string A token, which can be sent as ListFeatureViewsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

ListFeaturesRequest

Request message for FeaturestoreService.ListFeatures. Request message for FeatureRegistryService.ListFeatures.

Fields
parent	string Required. The resource name of the Location to list Features. Format for entity_type as parent: projects/{project}/locations/{location}/featurestores/{featurestore}/entityTypes/{entity_type} Format for feature_group as parent: projects/{project}/locations/{location}/featureGroups/{feature_group}
filter	string Lists the Features that match the filter expression. The following filters are supported: value_type: Supports = and != comparisons. create_time: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format. update_time: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format. labels: Supports key-value equality as well as key presence. Examples: value_type = DOUBLE --> Features whose type is DOUBLE. create_time > \"2020-01-31T15:30:00.000000Z\" OR update_time > \"2020-01-31T15:30:00.000000Z\" --> EntityTypes created or updated after 2020-01-31T15:30:00.000000Z. labels.active = yes AND labels.env = prod --> Features having both (active: yes) and (env: prod) labels. labels.env: * --> Any Feature which has a label with 'env' as the key.
page_size	int32 The maximum number of Features to return. The service may return fewer than this value. If unspecified, at most 1000 Features will be returned. The maximum value is 1000; any value greater than 1000 will be coerced to 1000.
page_token	string A page token, received from a previous FeaturestoreService.ListFeatures call or FeatureRegistryService.ListFeatures call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to FeaturestoreService.ListFeatures or FeatureRegistryService.ListFeatures must match the call that provided the page token.
order_by	string A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: feature_id value_type (Not supported for FeatureRegistry Feature) create_time update_time
read_mask	FieldMask Mask specifying which fields to read.
latest_stats_count	int32 Only applicable for Agent Platform Feature Store (Legacy). If set, return the most recent ListFeaturesRequest.latest_stats_count of stats for each Feature in response. Valid value is [0, 10]. If number of stats exists < ListFeaturesRequest.latest_stats_count, return all existing stats.

ListFeaturesResponse

Response message for FeaturestoreService.ListFeatures. Response message for FeatureRegistryService.ListFeatures.

Fields
features[]	Feature The Features matching the request.
next_page_token	string A token, which can be sent as ListFeaturesRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

ListFeaturestoresRequest

Request message for FeaturestoreService.ListFeaturestores.

Fields
parent	string Required. The resource name of the Location to list Featurestores. Format: projects/{project}/locations/{location}
filter	string Lists the featurestores that match the filter expression. The following fields are supported: create_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format. update_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format. online_serving_config.fixed_node_count: Supports =, !=, <, >, <=, and >= comparisons. labels: Supports key-value equality and key presence. Examples: create_time > "2020-01-01" OR update_time > "2020-01-01" Featurestores created or updated after 2020-01-01. labels.env = "prod" Featurestores with label "env" set to "prod".
page_size	int32 The maximum number of Featurestores to return. The service may return fewer than this value. If unspecified, at most 100 Featurestores will be returned. The maximum value is 100; any value greater than 100 will be coerced to 100.
page_token	string A page token, received from a previous FeaturestoreService.ListFeaturestores call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to FeaturestoreService.ListFeaturestores must match the call that provided the page token.
order_by	string A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported Fields: create_time update_time online_serving_config.fixed_node_count
read_mask	FieldMask Mask specifying which fields to read.

ListFeaturestoresResponse

Response message for FeaturestoreService.ListFeaturestores.

Fields
featurestores[]	Featurestore The Featurestores matching the request.
next_page_token	string A token, which can be sent as ListFeaturestoresRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

ListHyperparameterTuningJobsRequest

Request message for JobService.ListHyperparameterTuningJobs.

Fields
parent	string Required. The resource name of the Location to list the HyperparameterTuningJobs from. Format: projects/{project}/locations/{location}
filter	string The standard list filter. Supported fields: display_name supports =, != comparisons, and : wildcard. state supports =, != comparisons. create_time supports =, !=,<, <=,>, >= comparisons. create_time must be in RFC 3339 format. labels supports general map functions that is: labels.key=value - key:value equality `labels.key:* - key existence Some examples of using the filter are: state="JOB_STATE_SUCCEEDED" AND display_name:"my_job_*" state!="JOB_STATE_FAILED" OR display_name="my_job" NOT display_name="my_job" create_time>"2021-05-18T00:00:00Z" labels.keyA=valueA labels.keyB:*
page_size	int32 The standard list page size.
page_token	string The standard list page token. Typically obtained via ListHyperparameterTuningJobsResponse.next_page_token of the previous JobService.ListHyperparameterTuningJobs call.
read_mask	FieldMask Mask specifying which fields to read.

ListHyperparameterTuningJobsResponse

Response message for JobService.ListHyperparameterTuningJobs

Fields
hyperparameter_tuning_jobs[]	HyperparameterTuningJob List of HyperparameterTuningJobs in the requested page. HyperparameterTuningJob.trials of the jobs will be not be returned.
next_page_token	string A token to retrieve the next page of results. Pass to ListHyperparameterTuningJobsRequest.page_token to obtain that page.

ListIndexEndpointsRequest

Request message for IndexEndpointService.ListIndexEndpoints.

Fields
parent	string Required. The resource name of the Location from which to list the IndexEndpoints. Format: projects/{project}/locations/{location}
filter	string Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. index_endpoint supports = and !=. index_endpoint represents the IndexEndpoint ID, ie. the last segment of the IndexEndpoint's resourcename. display_name supports =, != and regex() (uses re2 syntax) labels supports general map functions that is: labels.key=value - key:value equality labels.key:* or labels:key - key existence A key including a space must be quoted.labels."a key"`. Some examples: * index_endpoint="1" * display_name="myDisplayName" * regex(display_name, "^A") -> The display name starts with an A. *labels.myKey="myValue"`
page_size	int32 Optional. The standard list page size.
page_token	string Optional. The standard list page token. Typically obtained via ListIndexEndpointsResponse.next_page_token of the previous IndexEndpointService.ListIndexEndpoints call.
read_mask	FieldMask Optional. Mask specifying which fields to read.

ListIndexEndpointsResponse

Response message for IndexEndpointService.ListIndexEndpoints.

Fields
index_endpoints[]	IndexEndpoint List of IndexEndpoints in the requested page.
next_page_token	string A token to retrieve next page of results. Pass to ListIndexEndpointsRequest.page_token to obtain that page.

ListIndexesRequest

Request message for IndexService.ListIndexes.

Fields
parent	string Required. The resource name of the Location from which to list the Indexes. Format: projects/{project}/locations/{location}
filter	string The standard list filter.
page_size	int32 The standard list page size.
page_token	string The standard list page token. Typically obtained via ListIndexesResponse.next_page_token of the previous IndexService.ListIndexes call.
read_mask	FieldMask Mask specifying which fields to read.

ListIndexesResponse

Response message for IndexService.ListIndexes.

Fields
indexes[]	Index List of indexes in the requested page.
next_page_token	string A token to retrieve next page of results. Pass to ListIndexesRequest.page_token to obtain that page.

ListMemoriesRequest

Request message for MemoryBankService.ListMemories.

Fields
parent	string Required. The resource name of the ReasoningEngine to list the Memories under. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}
filter	string Optional. The standard list filter. More detail in AIP-160. Supported fields: * scope (as a JSON string with equality match only) * topics (i.e. topics.custom_memory_topic_label: "example topic" OR topics.managed_memory_topic: USER_PREFERENCES)
page_size	int32 Optional. The standard list page size.
page_token	string Optional. The standard list page token.
order_by	string Optional. The standard list order by string. If not specified, the default order is create_time desc. If specified, the default sorting order of provided fields is ascending. More detail in AIP-132. Supported fields: * create_time * update_time

ListMemoriesResponse

Response message for MemoryBankService.ListMemories.

Fields
memories[]	Memory List of Memories in the requested page.
next_page_token	string A token to retrieve the next page of results. Pass to ListMemoriesRequest.page_token to obtain that page.

ListMetadataSchemasRequest

Request message for MetadataService.ListMetadataSchemas.

Fields
parent	string Required. The MetadataStore whose MetadataSchemas should be listed. Format: projects/{project}/locations/{location}/metadataStores/{metadatastore}
page_size	int32 The maximum number of MetadataSchemas to return. The service may return fewer. Must be in range 1-100, inclusive. Defaults to 100.
page_token	string A page token, received from a previous MetadataService.ListMetadataSchemas call. Provide this to retrieve the next page. When paginating, all other provided parameters must match the call that provided the page token. (Otherwise the request will fail with INVALID_ARGUMENT error.)
filter	string A query to filter available MetadataSchemas for matching results.

ListMetadataSchemasResponse

Response message for MetadataService.ListMetadataSchemas.

Fields
metadata_schemas[]	MetadataSchema The MetadataSchemas found for the MetadataStore.
next_page_token	string A token, which can be sent as ListMetadataSchemasRequest.page_token to retrieve the next page. If this field is not populated, there are no subsequent pages.

ListMetadataStoresRequest

Request message for MetadataService.ListMetadataStores.

Fields
parent	string Required. The Location whose MetadataStores should be listed. Format: projects/{project}/locations/{location}
page_size	int32 The maximum number of Metadata Stores to return. The service may return fewer. Must be in range 1-100, inclusive. Defaults to 100.
page_token	string A page token, received from a previous MetadataService.ListMetadataStores call. Provide this to retrieve the subsequent page. When paginating, all other provided parameters must match the call that provided the page token. (Otherwise the request will fail with INVALID_ARGUMENT error.)

ListMetadataStoresResponse

Response message for MetadataService.ListMetadataStores.

Fields
metadata_stores[]	MetadataStore The MetadataStores found for the Location.
next_page_token	string A token, which can be sent as ListMetadataStoresRequest.page_token to retrieve the next page. If this field is not populated, there are no subsequent pages.

ListModelDeploymentMonitoringJobsRequest

Request message for JobService.ListModelDeploymentMonitoringJobs.

Fields
parent	string Required. The parent of the ModelDeploymentMonitoringJob. Format: projects/{project}/locations/{location}
filter	string The standard list filter. Supported fields: display_name supports =, != comparisons, and : wildcard. state supports =, != comparisons. create_time supports =, !=,<, <=,>, >= comparisons. create_time must be in RFC 3339 format. labels supports general map functions that is: labels.key=value - key:value equality `labels.key:* - key existence Some examples of using the filter are: state="JOB_STATE_SUCCEEDED" AND display_name:"my_job_*" state!="JOB_STATE_FAILED" OR display_name="my_job" NOT display_name="my_job" create_time>"2021-05-18T00:00:00Z" labels.keyA=valueA labels.keyB:*
page_size	int32 The standard list page size.
page_token	string The standard list page token.
read_mask	FieldMask Mask specifying which fields to read

ListModelDeploymentMonitoringJobsResponse

Response message for JobService.ListModelDeploymentMonitoringJobs.

Fields
model_deployment_monitoring_jobs[]	ModelDeploymentMonitoringJob A list of ModelDeploymentMonitoringJobs that matches the specified filter in the request.
next_page_token	string The standard List next-page token.

ListModelEvaluationSlicesRequest

Request message for ModelService.ListModelEvaluationSlices.

Fields
parent	string Required. The resource name of the ModelEvaluation to list the ModelEvaluationSlices from. Format: projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}
filter	string The standard list filter. slice.dimension - for =.
page_size	int32 The standard list page size.
page_token	string The standard list page token. Typically obtained via ListModelEvaluationSlicesResponse.next_page_token of the previous ModelService.ListModelEvaluationSlices call.
read_mask	FieldMask Mask specifying which fields to read.

ListModelEvaluationSlicesResponse

Response message for ModelService.ListModelEvaluationSlices.

Fields
model_evaluation_slices[]	ModelEvaluationSlice List of ModelEvaluations in the requested page.
next_page_token	string A token to retrieve next page of results. Pass to ListModelEvaluationSlicesRequest.page_token to obtain that page.

ListModelEvaluationsRequest

Request message for ModelService.ListModelEvaluations.

Fields
parent	string Required. The resource name of the Model to list the ModelEvaluations from. Format: projects/{project}/locations/{location}/models/{model}
filter	string The standard list filter.
page_size	int32 The standard list page size.
page_token	string The standard list page token. Typically obtained via ListModelEvaluationsResponse.next_page_token of the previous ModelService.ListModelEvaluations call.
read_mask	FieldMask Mask specifying which fields to read.

ListModelEvaluationsResponse

Response message for ModelService.ListModelEvaluations.

Fields
model_evaluations[]	ModelEvaluation List of ModelEvaluations in the requested page.
next_page_token	string A token to retrieve next page of results. Pass to ListModelEvaluationsRequest.page_token to obtain that page.

ListModelMonitoringJobsRequest

Request message for ModelMonitoringService.ListModelMonitoringJobs.

Fields
parent	string Required. The parent of the ModelMonitoringJob. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}
filter	string The standard list filter. More detail in AIP-160.
page_size	int32 The standard list page size.
page_token	string The standard list page token.
read_mask	FieldMask Mask specifying which fields to read

ListModelMonitoringJobsResponse

Response message for ModelMonitoringService.ListModelMonitoringJobs.

Fields
model_monitoring_jobs[]	ModelMonitoringJob A list of ModelMonitoringJobs that matches the specified filter in the request.
next_page_token	string The standard List next-page token.

ListModelMonitorsRequest

Request message for ModelMonitoringService.ListModelMonitors.

Fields
parent	string Required. The resource name of the Location to list the ModelMonitors from. Format: projects/{project}/locations/{location}
filter	string The standard list filter. More detail in AIP-160.
page_size	int32 The standard list page size.
page_token	string The standard list page token.
read_mask	FieldMask Mask specifying which fields to read.

ListModelMonitorsResponse

Response message for ModelMonitoringService.ListModelMonitors

Fields
model_monitors[]	ModelMonitor List of ModelMonitor in the requested page.
next_page_token	string A token to retrieve the next page of results. Pass to ListModelMonitorsRequest.page_token to obtain that page.

ListModelVersionCheckpointsRequest

Request message for ModelService.ListModelVersionCheckpoints.

Fields
name	string Required. The name of the model version to list checkpoints for. projects/{project}/locations/{location}/models/{model}@{version} Example: projects/{project}/locations/{location}/models/{model}@2 or projects/{project}/locations/{location}/models/{model}@golden If no version ID or alias is specified, the latest version will be used.
page_size	int32 Optional. The standard list page size.
page_token	string Optional. The standard list page token. Typically obtained via next_page_token of the previous ListModelVersionCheckpoints call.

ListModelVersionCheckpointsResponse

Response message for ModelService.ListModelVersionCheckpoints

Fields
checkpoints[]	ModelVersionCheckpoint List of Model Version checkpoints.
next_page_token	string A token to retrieve the next page of results. Pass to ListModelVersionCheckpointsRequest.page_token to obtain that page.

ListModelVersionsRequest

Request message for ModelService.ListModelVersions.

Fields
name	string Required. The name of the model to list versions for.
page_size	int32 The standard list page size.
page_token	string The standard list page token. Typically obtained via next_page_token of the previous ListModelVersions call.
filter	string An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. labels supports general map functions that is: labels.key=value - key:value equality `labels.key:* or labels:key - key existence A key including a space must be quoted. labels."a key". Some examples: labels.myKey="myValue"
read_mask	FieldMask Mask specifying which fields to read.
order_by	string A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: create_time update_time Example: update_time asc, create_time desc.

ListModelVersionsResponse

Response message for ModelService.ListModelVersions

Fields
models[]	Model List of Model versions in the requested page. In the returned Model name field, version ID instead of regvision tag will be included.
next_page_token	string A token to retrieve the next page of results. Pass to ListModelVersionsRequest.page_token to obtain that page.

ListModelsRequest

Request message for ModelService.ListModels.

Fields
parent	string Required. The resource name of the Location to list the Models from. Format: projects/{project}/locations/{location}
filter	string An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. model supports = and !=. model represents the Model ID, i.e. the last segment of the Model's resource name. display_name supports = and != labels supports general map functions that is: labels.key=value - key:value equality `labels.key:* or labels:key - key existence A key including a space must be quoted. labels."a key". base_model_name only supports = Some examples: model=1234 displayName="myDisplayName" labels.myKey="myValue" baseModelName="text-bison"
page_size	int32 The standard list page size.
page_token	string The standard list page token. Typically obtained via ListModelsResponse.next_page_token of the previous ModelService.ListModels call.
read_mask	FieldMask Mask specifying which fields to read.

ListModelsResponse

Response message for ModelService.ListModels

Fields
models[]	Model List of Models in the requested page.
next_page_token	string A token to retrieve next page of results. Pass to ListModelsRequest.page_token to obtain that page.

ListNotebookExecutionJobsRequest

Request message for [NotebookService.ListNotebookExecutionJobs]

Fields
parent	string Required. The resource name of the Location from which to list the NotebookExecutionJobs. Format: projects/{project}/locations/{location}
filter	string Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. notebookExecutionJob supports = and !=. notebookExecutionJob represents the NotebookExecutionJob ID. displayName supports = and != and regex. schedule supports = and != and regex. Some examples: * notebookExecutionJob="123" * notebookExecutionJob="my-execution-job" * displayName="myDisplayName" and displayName=~"myDisplayNameRegex"
page_size	int32 Optional. The standard list page size.
page_token	string Optional. The standard list page token. Typically obtained via ListNotebookExecutionJobsResponse.next_page_token of the previous NotebookService.ListNotebookExecutionJobs call.
order_by	string Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: display_name create_time update_time Example: display_name, create_time desc.
view	NotebookExecutionJobView Optional. The NotebookExecutionJob view. Defaults to BASIC.

ListNotebookExecutionJobsResponse

Response message for [NotebookService.CreateNotebookExecutionJob]

Fields
notebook_execution_jobs[]	NotebookExecutionJob List of NotebookExecutionJobs in the requested page.
next_page_token	string A token to retrieve next page of results. Pass to ListNotebookExecutionJobsRequest.page_token to obtain that page.

ListNotebookRuntimeTemplatesRequest

Request message for NotebookService.ListNotebookRuntimeTemplates.

Fields
parent	string Required. The resource name of the Location from which to list the NotebookRuntimeTemplates. Format: projects/{project}/locations/{location}
filter	string Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. notebookRuntimeTemplate supports = and !=. notebookRuntimeTemplate represents the NotebookRuntimeTemplate ID, i.e. the last segment of the NotebookRuntimeTemplate's resource name. display_name supports = and != labels supports general map functions that is: labels.key=value - key:value equality `labels.key:* or labels:key - key existence A key including a space must be quoted. labels."a key". notebookRuntimeType supports = and !=. notebookRuntimeType enum: [USER_DEFINED, ONE_CLICK]. machineType supports = and !=. acceleratorType supports = and !=. Some examples: notebookRuntimeTemplate=notebookRuntimeTemplate123 displayName="myDisplayName" labels.myKey="myValue" notebookRuntimeType=USER_DEFINED machineType=e2-standard-4 acceleratorType=NVIDIA_TESLA_T4
page_size	int32 Optional. The standard list page size.
page_token	string Optional. The standard list page token. Typically obtained via ListNotebookRuntimeTemplatesResponse.next_page_token of the previous NotebookService.ListNotebookRuntimeTemplates call.
read_mask	FieldMask Optional. Mask specifying which fields to read.
order_by	string Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: display_name create_time update_time Example: display_name, create_time desc.

ListNotebookRuntimeTemplatesResponse

Response message for NotebookService.ListNotebookRuntimeTemplates.

Fields
notebook_runtime_templates[]	NotebookRuntimeTemplate List of NotebookRuntimeTemplates in the requested page.
next_page_token	string A token to retrieve next page of results. Pass to ListNotebookRuntimeTemplatesRequest.page_token to obtain that page.

ListNotebookRuntimesRequest

Request message for NotebookService.ListNotebookRuntimes.

Fields
parent	string Required. The resource name of the Location from which to list the NotebookRuntimes. Format: projects/{project}/locations/{location}
filter	string Optional. An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. notebookRuntime supports = and !=. notebookRuntime represents the NotebookRuntime ID, i.e. the last segment of the NotebookRuntime's resource name. displayName supports = and != and regex. notebookRuntimeTemplate supports = and !=. notebookRuntimeTemplate represents the NotebookRuntimeTemplate ID, i.e. the last segment of the NotebookRuntimeTemplate's resource name. healthState supports = and !=. healthState enum: [HEALTHY, UNHEALTHY, HEALTH_STATE_UNSPECIFIED]. runtimeState supports = and !=. runtimeState enum: [RUNTIME_STATE_UNSPECIFIED, RUNNING, BEING_STARTED, BEING_STOPPED, STOPPED, BEING_UPGRADED, ERROR, INVALID]. runtimeUser supports = and !=. API version is UI only: uiState supports = and !=. uiState enum: [UI_RESOURCE_STATE_UNSPECIFIED, UI_RESOURCE_STATE_BEING_CREATED, UI_RESOURCE_STATE_ACTIVE, UI_RESOURCE_STATE_BEING_DELETED, UI_RESOURCE_STATE_CREATION_FAILED]. notebookRuntimeType supports = and !=. notebookRuntimeType enum: [USER_DEFINED, ONE_CLICK]. machineType supports = and !=. acceleratorType supports = and !=. Some examples: notebookRuntime="notebookRuntime123" displayName="myDisplayName" and displayName=~"myDisplayNameRegex" notebookRuntimeTemplate="notebookRuntimeTemplate321" healthState=HEALTHY runtimeState=RUNNING runtimeUser="[e-mail]" uiState=UI_RESOURCE_STATE_BEING_DELETED notebookRuntimeType=USER_DEFINED machineType=e2-standard-4 acceleratorType=NVIDIA_TESLA_T4
page_size	int32 Optional. The standard list page size.
page_token	string Optional. The standard list page token. Typically obtained via ListNotebookRuntimesResponse.next_page_token of the previous NotebookService.ListNotebookRuntimes call.
read_mask	FieldMask Optional. Mask specifying which fields to read.
order_by	string Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: display_name create_time update_time Example: display_name, create_time desc.

ListNotebookRuntimesResponse

Response message for NotebookService.ListNotebookRuntimes.

Fields
notebook_runtimes[]	NotebookRuntime List of NotebookRuntimes in the requested page.
next_page_token	string A token to retrieve next page of results. Pass to ListNotebookRuntimesRequest.page_token to obtain that page.

ListOnlineEvaluatorsRequest

Request message for ListOnlineEvaluators.

Fields
parent	string Required. The parent resource of the OnlineEvaluators to list. Format: projects/{project}/locations/{location}.
page_size	int32 Optional. The maximum number of OnlineEvaluators to return. The service may return fewer than this value. If unspecified, at most 100 OnlineEvaluators will be returned. The maximum value is 100; values above 100 will be coerced to 100. Based on aip.dev/158.
page_token	string Optional. A token identifying a page of results the server should return. Based on aip.dev/158.
filter	string Optional. Standard list filter. Supported fields: * create_time * update_time * agent_resource Example: create_time>"2026-01-01T00:00:00-04:00" where the timestamp is in RFC 3339 format) Based on aip.dev/160.
order_by	string Optional. A comma-separated list of fields to order by. The default sorting order is ascending. Use "desc" after a field name for descending. Supported fields: * create_time * update_time Example: create_time desc. Based on aip.dev/132.

ListOnlineEvaluatorsResponse

Response message for ListOnlineEvaluators.

Fields
online_evaluators[]	OnlineEvaluator A list of OnlineEvaluators matching the request.
next_page_token	string A token to retrieve the next page. Absence of this field indicates there are no subsequent pages.

ListOptimalTrialsRequest

Request message for VizierService.ListOptimalTrials.

Fields
parent	string Required. The name of the Study that the optimal Trial belongs to.

ListOptimalTrialsResponse

Response message for VizierService.ListOptimalTrials.

Fields
optimal_trials[]	Trial The pareto-optimal Trials for multiple objective Study or the optimal trial for single objective Study. The definition of pareto-optimal can be checked in wiki page. https://en.wikipedia.org/wiki/Pareto_efficiency

ListPersistentResourcesRequest

Request message for PersistentResourceService.ListPersistentResources.

Fields
parent	string Required. The resource name of the Location to list the PersistentResources from. Format: projects/{project}/locations/{location}
page_size	int32 Optional. The standard list page size.
page_token	string Optional. The standard list page token. Typically obtained via ListPersistentResourcesResponse.next_page_token of the previous [PersistentResourceService.ListPersistentResource][] call.

ListPersistentResourcesResponse

Response message for PersistentResourceService.ListPersistentResources

Fields
persistent_resources[]	PersistentResource
next_page_token	string A token to retrieve next page of results. Pass to ListPersistentResourcesRequest.page_token to obtain that page.

ListPipelineJobsRequest

Request message for PipelineService.ListPipelineJobs.

Fields
parent	string Required. The resource name of the Location to list the PipelineJobs from. Format: projects/{project}/locations/{location}
filter	string Lists the PipelineJobs that match the filter expression. The following fields are supported: pipeline_name: Supports = and != comparisons. display_name: Supports =, != comparisons, and : wildcard. pipeline_job_user_id: Supports =, != comparisons, and : wildcard. for example, can check if pipeline's display_name contains step by doing display_name:"*step*" state: Supports = and != comparisons. create_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format. update_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format. end_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format. labels: Supports key-value equality and key presence. template_uri: Supports =, != comparisons, and : wildcard. template_metadata.version: Supports =, != comparisons, and : wildcard. Filter expressions can be combined together using logical operators (AND & OR). For example: pipeline_name="test" AND create_time>"2020-05-18T13:30:00Z". The syntax to define filter expression is based on https://google.aip.dev/160. Examples: create_time>"2021-05-18T00:00:00Z" OR update_time>"2020-05-18T00:00:00Z" PipelineJobs created or updated after 2020-05-18 00:00:00 UTC. labels.env = "prod" PipelineJobs with label "env" set to "prod".
page_size	int32 The standard list page size.
page_token	string The standard list page token. Typically obtained via ListPipelineJobsResponse.next_page_token of the previous PipelineService.ListPipelineJobs call.
order_by	string A comma-separated list of fields to order by. The default sort order is in ascending order. Use "desc" after a field name for descending. You can have multiple order_by fields provided e.g. "create_time desc, end_time", "end_time, start_time, update_time" For example, using "create_time desc, end_time" will order results by create time in descending order, and if there are multiple jobs having the same create time, order them by the end time in ascending order. if order_by is not specified, it will order by default order is create time in descending order. Supported fields: create_time update_time end_time start_time
read_mask	FieldMask Mask specifying which fields to read.

ListPipelineJobsResponse

Response message for PipelineService.ListPipelineJobs

Fields
pipeline_jobs[]	PipelineJob List of PipelineJobs in the requested page.
next_page_token	string A token to retrieve the next page of results. Pass to ListPipelineJobsRequest.page_token to obtain that page.

ListPublisherModelsRequest

Request message for ModelGardenService.ListPublisherModels.

Fields
parent	string Required. The name of the Publisher from which to list the PublisherModels. Format: publishers/{publisher}
filter	string Optional. The standard list filter.
page_size	int32 Optional. The standard list page size.
page_token	string Optional. The standard list page token. Typically obtained via ListPublisherModelsResponse.next_page_token of the previous ModelGardenService.ListPublisherModels call.
view	PublisherModelView Optional. PublisherModel view specifying which fields to read.
order_by	string Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending.
language_code	string Optional. The IETF BCP-47 language code representing the language in which the publisher models' text information should be written in. If not set, by default English (en).
list_all_versions	bool Optional. List all publisher model versions if the flag is set to true.

ListPublisherModelsResponse

Response message for ModelGardenService.ListPublisherModels.

Fields
publisher_models[]	PublisherModel List of PublisherModels in the requested page.
next_page_token	string A token to retrieve next page of results. Pass to [ListPublisherModels.page_token][] to obtain that page.

ListRagCorporaRequest

Request message for VertexRagDataService.ListRagCorpora.

Fields
parent	string Required. The resource name of the Location from which to list the RagCorpora. Format: projects/{project}/locations/{location}
page_size	int32 Optional. The standard list page size. The maximum value is 100. If not specified, a default value of 100 will be used.
page_token	string Optional. The standard list page token. Typically obtained via ListRagCorporaResponse.next_page_token of the previous VertexRagDataService.ListRagCorpora call.

ListRagCorporaResponse

Response message for VertexRagDataService.ListRagCorpora.

Fields
rag_corpora[]	RagCorpus List of RagCorpora in the requested page.
next_page_token	string A token to retrieve the next page of results. Pass to ListRagCorporaRequest.page_token to obtain that page.

ListRagDataSchemasRequest

Request message for VertexRagDataService.ListRagDataSchemas.

Fields
parent	string Required. The resource name of the RagCorpus from which to list the RagDataSchemas. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}
page_size	int32 Optional. The standard list page size. The maximum value is 100. If not specified, a default value of 100 will be used.
page_token	string Optional. The standard list page token. Typically obtained via ListRagDataSchemasResponse.next_page_token of the previous VertexRagDataService.ListRagDataSchemas call.

ListRagDataSchemasResponse

Response message for VertexRagDataService.ListRagDataSchemas.

Fields
rag_data_schemas[]	RagDataSchema List of RagDataSchemas in the requested page.
next_page_token	string A token to retrieve the next page of results. Pass to ListRagDataSchemasRequest.page_token to obtain that page.

ListRagFilesRequest

Request message for VertexRagDataService.ListRagFiles.

Fields
parent	string Required. The resource name of the RagCorpus from which to list the RagFiles. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}
page_size	int32 Optional. The standard list page size. The maximum value is 100. If not specified, a default value of 100 will be used.
page_token	string Optional. The standard list page token. Typically obtained via ListRagFilesResponse.next_page_token of the previous VertexRagDataService.ListRagFiles call.

ListRagFilesResponse

Response message for VertexRagDataService.ListRagFiles.

Fields
rag_files[]	RagFile List of RagFiles in the requested page.
next_page_token	string A token to retrieve the next page of results. Pass to ListRagFilesRequest.page_token to obtain that page.

ListRagMetadataRequest

Request message for VertexRagDataService.ListRagMetadata.

Fields
parent	string Required. The resource name of the RagFile from which to list the RagMetadata. Format: projects/{project}/locations/{location}/ragCorpora/{rag_corpus}/ragFiles/{rag_file}
page_size	int32 Optional. The standard list page size. The maximum value is 100. If not specified, a default value of 100 will be used.
page_token	string Optional. The standard list page token. Typically obtained via ListRagMetadataResponse.next_page_token of the previous VertexRagDataService.ListRagMetadata call.

ListRagMetadataResponse

Response message for VertexRagDataService.ListRagMetadata.

Fields
rag_metadata[]	RagMetadata List of RagMetadata in the requested page.
next_page_token	string A token to retrieve the next page of results. Pass to ListRagMetadataRequest.page_token to obtain that page.

ListReasoningEngineRuntimeRevisionsRequest

Request message for ReasoningEngineRuntimeRevisionService.ListReasoningEngineRuntimeRevisions.

Fields
parent	string Required. The resource name of the ReasoningEngine to list the ReasoningEngineRuntimeRevisions from. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}
filter	string Optional. The standard list filter. More detail in AIP-160.
page_size	int32 Optional. The maximum number of ReasoningEngineRuntimeRevisions to return. The service may return fewer than this value. If unspecified, at most 50 revisions will be returned. The maximum value is 100; values above 100 will be coerced to 100.
page_token	string Optional. The standard list page token.

ListReasoningEngineRuntimeRevisionsResponse

Response message for ReasoningEngineRuntimeRevisionService.ListReasoningEngineRuntimeRevisions

Fields
reasoning_engine_runtime_revisions[]	ReasoningEngineRuntimeRevision List of ReasoningEngineRuntimeRevisions in the requested page.
next_page_token	string A token to retrieve the next page of results. Pass to ListReasoningEngineRuntimeRevisionsRequest.page_token to obtain that page.

ListReasoningEnginesRequest

Request message for ReasoningEngineService.ListReasoningEngines.

Fields
parent	string Required. The resource name of the Location to list the ReasoningEngines from. Format: projects/{project}/locations/{location}
filter	string Optional. The standard list filter. More detail in AIP-160.
page_size	int32 Optional. The standard list page size.
page_token	string Optional. The standard list page token.

ListReasoningEnginesResponse

Response message for ReasoningEngineService.ListReasoningEngines

Fields
reasoning_engines[]	ReasoningEngine List of ReasoningEngines in the requested page.
next_page_token	string A token to retrieve the next page of results. Pass to ListReasoningEnginesRequest.page_token to obtain that page.

ListSavedQueriesRequest

Request message for DatasetService.ListSavedQueries.

Fields
parent	string Required. The resource name of the Dataset to list SavedQueries from. Format: projects/{project}/locations/{location}/datasets/{dataset}
filter	string The standard list filter.
page_size	int32 The standard list page size.
page_token	string The standard list page token.
read_mask	FieldMask Mask specifying which fields to read.
order_by	string A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending.

ListSavedQueriesResponse

Response message for DatasetService.ListSavedQueries.

Fields
saved_queries[]	SavedQuery A list of SavedQueries that match the specified filter in the request.
next_page_token	string The standard List next-page token.

ListSchedulesRequest

Request message for ScheduleService.ListSchedules.

Fields
parent	string Required. The resource name of the Location to list the Schedules from. Format: projects/{project}/locations/{location}
filter	string Lists the Schedules that match the filter expression. The following fields are supported: display_name: Supports =, != comparisons, and : wildcard. state: Supports = and != comparisons. request: Supports existence of the check. (e.g. create_pipeline_job_request:* --> Schedule has create_pipeline_job_request). create_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format. start_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format. end_time: Supports =, !=, <, >, <=, >= comparisons and :* existence check. Values must be in RFC 3339 format. next_run_time: Supports =, !=, <, >, <=, and >= comparisons. Values must be in RFC 3339 format. Filter expressions can be combined together using logical operators (NOT, AND & OR). The syntax to define filter expression is based on https://google.aip.dev/160. Examples: state="ACTIVE" AND display_name:"my_schedule_*" NOT display_name="my_schedule" create_time>"2021-05-18T00:00:00Z" end_time>"2021-05-18T00:00:00Z" OR NOT end_time:* create_pipeline_job_request:*
page_size	int32 The standard list page size. Default to 100 if not specified.
page_token	string The standard list page token. Typically obtained via ListSchedulesResponse.next_page_token of the previous ScheduleService.ListSchedules call.
order_by	string A comma-separated list of fields to order by. The default sort order is in ascending order. Use "desc" after a field name for descending. You can have multiple order_by fields provided. For example, using "create_time desc, end_time" will order results by create time in descending order, and if there are multiple schedules having the same create time, order them by the end time in ascending order. If order_by is not specified, it will order by default with create_time in descending order. Supported fields: create_time start_time end_time next_run_time

ListSchedulesResponse

Response message for ScheduleService.ListSchedules

Fields
schedules[]	Schedule List of Schedules in the requested page.
next_page_token	string A token to retrieve the next page of results. Pass to ListSchedulesRequest.page_token to obtain that page.

ListSessionsRequest

Request message for SessionService.ListSessions.

Fields
parent	string Required. The resource name of the location to list sessions from. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}
page_size	int32 Optional. The maximum number of sessions to return. The service may return fewer than this value. If unspecified, the default page size is 100. Values greater than 100 will be capped at 100.
page_token	string Optional. The next_page_token value returned from a previous list SessionService.ListSessions call.
filter	string Optional. The standard list filter. Supported fields: * display_name * user_id * labels Example: display_name="abc", user_id="123", labels.key="value".
order_by	string Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: * create_time * update_time Example: create_time desc.

ListSessionsResponse

Response message for SessionService.ListSessions.

Fields
sessions[]	Session A list of sessions matching the request.
next_page_token	string A token, which can be sent as ListSessionsRequest.page_token to retrieve the next page. Absence of this field indicates there are no subsequent pages.

ListSkillRevisionsRequest

Request message for SkillRegistryService.ListSkillRevisions.

Fields
parent	string Required. The resource name of the Skill to list revisions for. Format: projects/{project}/locations/{location}/skills/{skill}
page_size	int32 Optional. The standard list page size.
page_token	string Optional. The standard list page token.
filter	string Optional. The standard list filter. More detail in AIP-160. Supported fields (equality match only): * labels

ListSkillRevisionsResponse

Response message for SkillRegistryService.ListSkillRevisions.

Fields
skill_revisions[]	SkillRevision The list of Skill Revisions in the request page.
next_page_token	string A token, which can be sent as page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

ListSkillsRequest

Request message for SkillRegistryService.ListSkills.

Fields
parent	string Required. The location to list the Skills in. Format: projects/{project}/locations/{location}
page_size	int32 Optional. The standard list page size.
page_token	string Optional. The standard list page token.

ListSkillsResponse

Response message for SkillRegistryService.ListSkills.

Fields
skills[]	Skill The Skills.
next_page_token	string A token, which can be sent as ListSkillsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

ListSpecialistPoolsRequest

Request message for SpecialistPoolService.ListSpecialistPools.

Fields
parent	string Required. The name of the SpecialistPool's parent resource. Format: projects/{project}/locations/{location}
page_size	int32 The standard list page size.
page_token	string The standard list page token. Typically obtained by ListSpecialistPoolsResponse.next_page_token of the previous SpecialistPoolService.ListSpecialistPools call. Return first page if empty.
read_mask	FieldMask Mask specifying which fields to read. FieldMask represents a set of

ListSpecialistPoolsResponse

Response message for SpecialistPoolService.ListSpecialistPools.

Fields
specialist_pools[]	SpecialistPool A list of SpecialistPools that matches the specified filter in the request.
next_page_token	string The standard List next-page token.

ListStudiesRequest

Request message for VizierService.ListStudies.

Fields
parent	string Required. The resource name of the Location to list the Study from. Format: projects/{project}/locations/{location}
page_token	string Optional. A page token to request the next page of results. If unspecified, there are no subsequent pages.
page_size	int32 Optional. The maximum number of studies to return per "page" of results. If unspecified, service will pick an appropriate default.

ListStudiesResponse

Response message for VizierService.ListStudies.

Fields
studies[]	Study The studies associated with the project.
next_page_token	string Passes this token as the page_token field of the request for a subsequent call. If this field is omitted, there are no subsequent pages.

ListTensorboardExperimentsRequest

Request message for TensorboardService.ListTensorboardExperiments.

Fields
parent	string Required. The resource name of the Tensorboard to list TensorboardExperiments. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
filter	string Lists the TensorboardExperiments that match the filter expression.
page_size	int32 The maximum number of TensorboardExperiments to return. The service may return fewer than this value. If unspecified, at most 50 TensorboardExperiments are returned. The maximum value is 1000; values above 1000 are coerced to 1000.
page_token	string A page token, received from a previous TensorboardService.ListTensorboardExperiments call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to TensorboardService.ListTensorboardExperiments must match the call that provided the page token.
order_by	string Field to use to sort the list.
read_mask	FieldMask Mask specifying which fields to read.

ListTensorboardExperimentsResponse

Response message for TensorboardService.ListTensorboardExperiments.

Fields
tensorboard_experiments[]	TensorboardExperiment The TensorboardExperiments mathching the request.
next_page_token	string A token, which can be sent as ListTensorboardExperimentsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

ListTensorboardRunsRequest

Request message for TensorboardService.ListTensorboardRuns.

Fields
parent	string Required. The resource name of the TensorboardExperiment to list TensorboardRuns. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}
filter	string Lists the TensorboardRuns that match the filter expression.
page_size	int32 The maximum number of TensorboardRuns to return. The service may return fewer than this value. If unspecified, at most 50 TensorboardRuns are returned. The maximum value is 1000; values above 1000 are coerced to 1000.
page_token	string A page token, received from a previous TensorboardService.ListTensorboardRuns call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to TensorboardService.ListTensorboardRuns must match the call that provided the page token.
order_by	string Field to use to sort the list.
read_mask	FieldMask Mask specifying which fields to read.

ListTensorboardRunsResponse

Response message for TensorboardService.ListTensorboardRuns.

Fields
tensorboard_runs[]	TensorboardRun The TensorboardRuns mathching the request.
next_page_token	string A token, which can be sent as ListTensorboardRunsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

ListTensorboardTimeSeriesRequest

Request message for TensorboardService.ListTensorboardTimeSeries.

Fields
parent	string Required. The resource name of the TensorboardRun to list TensorboardTimeSeries. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}/experiments/{experiment}/runs/{run}
filter	string Lists the TensorboardTimeSeries that match the filter expression.
page_size	int32 The maximum number of TensorboardTimeSeries to return. The service may return fewer than this value. If unspecified, at most 50 TensorboardTimeSeries are returned. The maximum value is 1000; values above 1000 are coerced to 1000.
page_token	string A page token, received from a previous TensorboardService.ListTensorboardTimeSeries call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to TensorboardService.ListTensorboardTimeSeries must match the call that provided the page token.
order_by	string Field to use to sort the list.
read_mask	FieldMask Mask specifying which fields to read.

ListTensorboardTimeSeriesResponse

Response message for TensorboardService.ListTensorboardTimeSeries.

Fields
tensorboard_time_series[]	TensorboardTimeSeries The TensorboardTimeSeries mathching the request.
next_page_token	string A token, which can be sent as ListTensorboardTimeSeriesRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

ListTensorboardsRequest

Request message for TensorboardService.ListTensorboards.

Fields
parent	string Required. The resource name of the Location to list Tensorboards. Format: projects/{project}/locations/{location}
filter	string Lists the Tensorboards that match the filter expression.
page_size	int32 The maximum number of Tensorboards to return. The service may return fewer than this value. If unspecified, at most 100 Tensorboards are returned. The maximum value is 100; values above 100 are coerced to 100.
page_token	string A page token, received from a previous TensorboardService.ListTensorboards call. Provide this to retrieve the subsequent page. When paginating, all other parameters provided to TensorboardService.ListTensorboards must match the call that provided the page token.
order_by	string Field to use to sort the list.
read_mask	FieldMask Mask specifying which fields to read.

ListTensorboardsResponse

Response message for TensorboardService.ListTensorboards.

Fields
tensorboards[]	Tensorboard The Tensorboards mathching the request.
next_page_token	string A token, which can be sent as ListTensorboardsRequest.page_token to retrieve the next page. If this field is omitted, there are no subsequent pages.

ListTrainingPipelinesRequest

Request message for PipelineService.ListTrainingPipelines.

Fields
parent	string Required. The resource name of the Location to list the TrainingPipelines from. Format: projects/{project}/locations/{location}
filter	string The standard list filter. Supported fields: display_name supports =, != comparisons, and : wildcard. state supports =, != comparisons. training_task_definition =, != comparisons, and : wildcard. create_time supports =, !=,<, <=,>, >= comparisons. create_time must be in RFC 3339 format. labels supports general map functions that is: labels.key=value - key:value equality `labels.key:* - key existence Some examples of using the filter are: state="PIPELINE_STATE_SUCCEEDED" AND display_name:"my_pipeline_*" state!="PIPELINE_STATE_FAILED" OR display_name="my_pipeline" NOT display_name="my_pipeline" create_time>"2021-05-18T00:00:00Z" training_task_definition:"*automl_text_classification*"
page_size	int32 The standard list page size.
page_token	string The standard list page token. Typically obtained via ListTrainingPipelinesResponse.next_page_token of the previous PipelineService.ListTrainingPipelines call.
read_mask	FieldMask Mask specifying which fields to read.

ListTrainingPipelinesResponse

Response message for PipelineService.ListTrainingPipelines

Fields
training_pipelines[]	TrainingPipeline List of TrainingPipelines in the requested page.
next_page_token	string A token to retrieve the next page of results. Pass to ListTrainingPipelinesRequest.page_token to obtain that page.

ListTrialsRequest

Request message for VizierService.ListTrials.

Fields
parent	string Required. The resource name of the Study to list the Trial from. Format: projects/{project}/locations/{location}/studies/{study}
page_token	string Optional. A page token to request the next page of results. If unspecified, there are no subsequent pages.
page_size	int32 Optional. The number of Trials to retrieve per "page" of results. If unspecified, the service will pick an appropriate default.

ListTrialsResponse

Response message for VizierService.ListTrials.

Fields
trials[]	Trial The Trials associated with the Study.
next_page_token	string Pass this token as the page_token field of the request for a subsequent call. If this field is omitted, there are no subsequent pages.

ListTuningJobsRequest

Request message for GenAiTuningService.ListTuningJobs.

Fields
parent	string Required. The resource name of the location to list the tuning jobs from. Format: projects/{project}/locations/{location}
filter	string Optional. The standard list filter.
page_size	int32 Optional. The standard list page size.
page_token	string Optional. The standard list page token. Typically obtained from ListTuningJobsResponse.next_page_token of the previous GenAiTuningService.ListTuningJobs call.

ListTuningJobsResponse

Response message for GenAiTuningService.ListTuningJobs

Fields
tuning_jobs[]	TuningJob The tuning jobs that match the request.
next_page_token	string A token to retrieve the next page of results. Pass this token in a subsequent [GenAiTuningService.ListTuningJobs] call to retrieve the next page of results.

LogprobsResult

The log probabilities of the tokens generated by the model.

This is useful for understanding the model's confidence in its predictions and for debugging. For example, you can use log probabilities to identify when the model is making a less confident prediction or to explore alternative responses that the model considered. A low log probability can also indicate that the model is "hallucinating" or generating factually incorrect information.

Fields
top_candidates[]	TopCandidates A list of the top candidate tokens at each decoding step. The length of this list is equal to the total number of decoding steps.
chosen_candidates[]	Candidate A list of the chosen candidate tokens at each decoding step. The length of this list is equal to the total number of decoding steps. Note that the chosen candidate might not be in top_candidates.

Candidate

A single token and its associated log probability.

Fields
token	string The token's string representation.
token_id	int32 The token's numerical ID. While the token field provides the string representation of the token, the token_id is the numerical representation that the model uses internally. This can be useful for developers who want to build custom logic based on the model's vocabulary.
log_probability	float The log probability of this token. A higher value indicates that the model was more confident in this token. The log probability can be used to assess the relative likelihood of different tokens and to identify when the model is uncertain.

TopCandidates

A list of the top candidate tokens and their log probabilities at each decoding step. This can be used to see what other tokens the model considered.

Fields
candidates[]	Candidate The list of candidate tokens, sorted by log probability in descending order.

LookupStudyRequest

Request message for VizierService.LookupStudy.

Fields
parent	string Required. The resource name of the Location to get the Study from. Format: projects/{project}/locations/{location}
display_name	string Required. The user-defined display name of the Study

LossAnalysisConfig

Configuration for the loss analysis job.

Fields
metric	string Required. The metric to analyze (e.g., "tool_use_quality"). This filters the EvaluationItems in the EvalSet to only those where EvaluationResult.metric matches this value.
candidate	string Required. The candidate model/agent to analyze (e.g., "gemini-3.0-pro"). This targets the specific CandidateResult within the EvaluationResult.

LossAnalysisResult

The top-level result for loss analysis, stored within an EvalSet.

Fields
config	LossAnalysisConfig The configuration used to generate this analysis.
analysis_time	Timestamp The timestamp when this analysis was performed.
clusters[]	LossCluster The list of identified loss clusters.

LossCluster

Represents a semantic grouping of failures (e.g., "Hallucination of Action").

Fields
cluster_id	string Unique identifier for the loss cluster within the scope of the analysis result.
taxonomy_entry	LossTaxonomyEntry The structured definition of the loss taxonomy for this cluster.
item_count	int32 The total number of EvaluationItems falling into this cluster.
examples[]	LossExample A list of examples that belong to this cluster. This links the cluster back to the specific EvaluationItems and Rubrics.

LossExample

Represents a specific example of a loss pattern.

Fields
failed_rubrics[]	FailedRubric The specific rubric(s) that failed and caused this example to be classified here. An example might fail multiple rubrics, but only specific ones trigger this loss pattern.
Union field source. The source of this example. source can be only one of the following:
evaluation_item	string Reference to the persisted EvalItem resource name. Format: projects/.../locations/.../evaluationItems/{item_id} Used when analysis is run on an EvalSet.

LossTaxonomyEntry

Defines a specific entry in the loss pattern taxonomy.

Fields
l1_category	string The primary category of the loss (e.g., "Hallucination", "Tool Calling"). This field is typically required.
l2_category	string The secondary category of the loss (e.g., "Hallucination of Action", "Incorrect Tool Selection").
description	string A detailed description of this loss pattern. Example: "The agent verbally confirms an action without executing the tool."

LustreMount

Represents a mount configuration for Lustre file system.

Fields
instance_ip	string Required. IP address of the Lustre instance.
volume_handle	string Required. The unique identifier of the Lustre volume.
filesystem	string Required. The name of the Lustre filesystem.
mount_point	string Required. Destination mount path. The Lustre file system will be mounted for the user under /mnt/lustre/

MachineSpec

Specification of a single machine.

Fields
machine_type	string Immutable. The type of the machine. See the list of machine types supported for prediction See the list of machine types supported for custom training. For DeployedModel this field is optional, and the default value is n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
accelerator_type	AcceleratorType Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
accelerator_count	int32 The number of accelerators to attach to the machine. For accelerator optimized machine types, One may set the accelerator_count from 1 to N for machine with N GPUs. If accelerator_count is less than or equal to N / 2, Agent Platform co-schedules the replicas of the model into the same VM to save cost. For example, if the machine type is a3-highgpu-8g, which has 8 H100 GPUs, one can set accelerator_count to 1 to 8. If accelerator_count is 1, 2, 3, or 4, Agent Platform co-schedules 8, 4, 2, or 2 replicas of the model into the same VM to save cost. When co-scheduling, CPU, memory and storage on the VM will be distributed to replicas on the VM. For example, one can expect a co-scheduled replica requesting 2 GPUs out of a 8-GPU VM will receive 25% of the CPU, memory and storage of the VM. Note that the feature is not compatible with multihost_gpu_node_count. When multihost_gpu_node_count is set, the co-scheduling will not be enabled.
gpu_partition_size	string Optional. Immutable. The Nvidia GPU partition size. When specified, the requested accelerators will be partitioned into smaller GPU partitions. For example, if the request is for 8 units of NVIDIA A100 GPUs, and gpu_partition_size="1g.10gb", the service will create 8 * 7 = 56 partitioned MIG instances. The partition size must be a value supported by the requested accelerator. Refer to Nvidia GPU Partitioning for the available partition sizes. If set, the accelerator_count should be set to 1.
tpu_topology	string Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
multihost_gpu_node_count	int32 Optional. Immutable. The number of nodes per replica for multihost GPU deployments.
reservation_affinity	ReservationAffinity Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
min_gpu_driver_version	string Optional. Immutable. The minimum GPU driver version that this machine requires. For example, "535.104.06". If not specified, the default GPU driver version will be used by the underlying infrastructure.

ManualBatchTuningParameters

Manual batch tuning parameters.

Fields

batch_size

int32 Immutable. The number of the records (e.g. instances) of the operation given in each batch to a machine replica. Machine type, and size of a single record should be considered when setting this parameter, higher value speeds up the batch operation's execution, but too high value will result in a whole batch not fitting in a machine's memory, and the whole operation will fail. The default value is 64.

Measurement

A message representing a Measurement of a Trial. A Measurement contains the Metrics got by executing a Trial using suggested hyperparameter values.

Fields
elapsed_duration	Duration Output only. Time that the Trial has been running at the point of this Measurement.
step_count	int64 Output only. The number of steps the machine learning model has been trained for. Must be non-negative.
metrics[]	Metric Output only. A list of metrics got by evaluating the objective functions using suggested Parameter values.

Metric

A message representing a metric in the measurement.

Fields
metric_id	string Output only. The ID of the Metric. The Metric should be defined in StudySpec's Metrics.
value	double Output only. The value for this metric.

Memory

A memory.

Fields
name	string Identifier. Represents the resource name of the Memory. Format: projects/{project}/locations/{location}/reasoningEngines/{reasoning_engine}/memories/{memory}
display_name	string Optional. Represents the display name of the Memory.
description	string Optional. Represents the description of the Memory.
create_time	Timestamp Output only. Represents the timestamp when this Memory was created.
update_time	Timestamp Output only. Represents the timestamp when this Memory was most recently updated.
fact	string Optional. Represents semantic knowledge extracted from the source content.
scope	map<string, string> Required. Immutable. Represents the scope of the Memory. Memories are isolated within their scope. The scope is defined when creating or generating memories. Scope values cannot contain the wildcard character '*'.
revision_labels	map<string, string> Optional. Input only. Represents the labels to apply to the Memory Revision created as a result of this request.
memory_type	MemoryType Optional. Represents the type of the memory. If not set, the NATURAL_LANGUAGE_COLLECTION type is used. If STRUCTURED_COLLECTION or STRUCTURED_PROFILE is used, then structured_data must be provided.
structured_content	StructuredContent Optional. Represents the structured content of the memory.
Union field expiration. The expiration of the Memory. If not set, the Memory will not be automatically deleted. expiration can be only one of the following:
expire_time	Timestamp Optional. Represents the timestamp of when this resource is considered expired. This is always provided on output when expiration is set on input, regardless of whether expire_time or ttl was provided.
ttl	Duration Optional. Input only. Represents the TTL for this resource. The expiration time is computed: now + TTL.
Union field revision_expiration. (Input-only) The expiration of the Memory Revision created as a result of this request. If not set, Memory Bank will defer to MemoryBankConfig.memory_revision_default_ttl or the global default, 365 days. revision_expiration can be only one of the following:
revision_expire_time	Timestamp Optional. Input only. Represents the timestamp of when the revision is considered expired. If not set, the memory revision will be kept until manually deleted.
revision_ttl	Duration Optional. Input only. Represents the TTL for the revision. The expiration time is computed: now + TTL.
disable_memory_revisions	bool Optional. Input only. Indicates whether no revision will be created for this request.

StructuredContent

Represents the structured value of the memory.

Fields
data	Struct Required. Represents the structured value of the memory.
schema_id	string Required. Represents the schema ID for which this structured memory belongs to.

MemoryGenerationTriggerConfig

Represents configuration for triggering generation.

Fields
generation_rule	GenerationTriggerRule Optional. Represents the active rule that determines when to flush the buffer. If not set, then the stream will be force flushed immediately.

GenerationTriggerRule

Represents the active rule that determines when to flush the buffer.

Fields
event_count	int32 Optional. Specifies to trigger generation when the event count reaches this limit.
Union field time_based_condition. Represents the time based condition that triggers generation. time_based_condition can be only one of the following:
idle_duration	Duration Optional. Specifies to trigger generation if the stream is inactive for the specified duration after the most recent event. The duration must have a minute-level granularity.
fixed_interval	Duration Optional. Specifies to trigger generation at a fixed interval. The duration must have a minute-level granularity.

MemoryProfile

A memory profile.

Fields
schema_id	string Represents the ID of the schema. This ID corresponds to the schema_id defined inside the SchemaConfig, under StructuredMemoryCustomizationConfig.
profile	Struct Represents the profile data.

MemoryType

The type of Memory.

Enums
MEMORY_TYPE_UNSPECIFIED	Represents an unspecified memory type. This value should not be used.
NATURAL_LANGUAGE_COLLECTION	Indicates belonging to a collection of natural language memories.
STRUCTURED_PROFILE	Indicates belonging to a structured profile.

MergeVersionAliasesRequest

Request message for ModelService.MergeVersionAliases.

Fields
name	string Required. The name of the model version to merge aliases, with a version ID explicitly included. Example: projects/{project}/locations/{location}/models/{model}@1234
version_aliases[]	string Required. The set of version aliases to merge. The alias should be at most 128 characters, and match [a-z][a-zA-Z0-9-]{0,126}[a-z-0-9]. Add the - prefix to an alias means removing that alias from the version. - is NOT counted in the 128 characters. Example: -golden means removing the golden alias from the version. There is NO ordering in aliases, which means 1) The aliases returned from GetModel API might not have the exactly same order from this MergeVersionAliases API. 2) Adding and deleting the same alias in the request is not recommended, and the 2 operations will be cancelled out.

MetadataList

List representation in metadata.

Fields
values[]	MetadataValue The values of LIST data type metadata.

MetadataSchema

Instance of a general MetadataSchema.

Fields
name	string Output only. The resource name of the MetadataSchema.
schema_version	string The version of the MetadataSchema. The version's format must match the following regular expression: ^[0-9]+[.][0-9]+[.][0-9]+$, which would allow to order/compare different versions. Example: 1.0.0, 1.0.1, etc.
schema	string Required. The raw YAML string representation of the MetadataSchema. The combination of [MetadataSchema.version] and the schema name given by title in [MetadataSchema.schema] must be unique within a MetadataStore. The schema is defined as an OpenAPI 3.0.2 MetadataSchema Object
schema_type	MetadataSchemaType The type of the MetadataSchema. This is a property that identifies which metadata types will use the MetadataSchema.
create_time	Timestamp Output only. Timestamp when this MetadataSchema was created.
description	string Description of the Metadata Schema

MetadataSchemaType

Describes the type of the MetadataSchema.

Enums
METADATA_SCHEMA_TYPE_UNSPECIFIED	Unspecified type for the MetadataSchema.
ARTIFACT_TYPE	A type indicating that the MetadataSchema will be used by Artifacts.
EXECUTION_TYPE	A typee indicating that the MetadataSchema will be used by Executions.
CONTEXT_TYPE	A state indicating that the MetadataSchema will be used by Contexts.

MetadataStore

Instance of a metadata store. Contains a set of metadata that can be queried.

Fields
name	string Output only. The resource name of the MetadataStore instance.
create_time	Timestamp Output only. Timestamp when this MetadataStore was created.
update_time	Timestamp Output only. Timestamp when this MetadataStore was last updated.
encryption_spec	EncryptionSpec Customer-managed encryption key spec for a Metadata Store. If set, this Metadata Store and all sub-resources of this Metadata Store are secured using this key.
description	string Description of the MetadataStore.
state	MetadataStoreState Output only. State information of the MetadataStore.
dataplex_config	DataplexConfig Optional. Dataplex integration settings.

DataplexConfig

Represents Dataplex integration settings.

Fields
enabled_pipelines_lineage	bool Optional. Whether or not Data Lineage synchronization is enabled for Vertex Pipelines.

MetadataStoreState

Represents state information for a MetadataStore.

Fields
disk_utilization_bytes	int64 The disk utilization of the MetadataStore in bytes.

MetadataValue

Value of Metadata, including all types available in data schema.

Fields
Union field value. The value of the metadata. value can be only one of the following:
int_value	int64 Value of int type metadata.
float_value	float Value of float type metadata.
str_value	string Value of string type metadata.
datetime_value	string Value of date time type metadata.
bool_value	bool Value of boolean type metadata.
list_value	MetadataList Value of list type metadata.

Metric

The metric used for running evaluations.

Fields
aggregation_metrics[]	AggregationMetric Optional. The aggregation metrics to use.
metadata	MetricMetadata Optional. Metadata about the metric, used for visualization and organization.
Union field metric_spec. The spec for the metric. It would be either a pre-defined metric, or a inline metric spec. metric_spec can be only one of the following:
predefined_metric_spec	PredefinedMetricSpec The spec for a pre-defined metric.
computation_based_metric_spec	ComputationBasedMetricSpec Spec for a computation based metric.
llm_based_metric_spec	LLMBasedMetricSpec Spec for an LLM based metric.
custom_code_execution_spec	CustomCodeExecutionSpec Spec for Custom Code Execution metric.
pointwise_metric_spec	PointwiseMetricSpec Spec for pointwise metric.
pairwise_metric_spec	PairwiseMetricSpec Spec for pairwise metric.
exact_match_spec	ExactMatchSpec Spec for exact match metric.
bleu_spec	BleuSpec Spec for bleu metric.
rouge_spec	RougeSpec Spec for rouge metric.

AggregationMetric

The per-metric statistics on evaluation results supported by EvaluationService.EvaluateDataset.

Enums
AGGREGATION_METRIC_UNSPECIFIED	Unspecified aggregation metric.
AVERAGE	Average aggregation metric. Not supported for Pairwise metric.
MODE	Mode aggregation metric.
STANDARD_DEVIATION	Standard deviation aggregation metric. Not supported for pairwise metric.
VARIANCE	Variance aggregation metric. Not supported for pairwise metric.
MINIMUM	Minimum aggregation metric. Not supported for pairwise metric.
MAXIMUM	Maximum aggregation metric. Not supported for pairwise metric.
MEDIAN	Median aggregation metric. Not supported for pairwise metric.
PERCENTILE_P90	90th percentile aggregation metric. Not supported for pairwise metric.
PERCENTILE_P95	95th percentile aggregation metric. Not supported for pairwise metric.
PERCENTILE_P99	99th percentile aggregation metric. Not supported for pairwise metric.

MetricMetadata

Metadata about the metric, used for visualization and organization.

Fields
title	string Optional. The user-friendly name for the metric. If not set for a registered metric, it will default to the metric's display name.
score_range	ScoreRange Optional. The range of possible scores for this metric, used for plotting.
other_metadata	Struct Optional. Flexible metadata for user-defined attributes.

ScoreRange

The range of possible scores for this metric, used for plotting.

Fields
description	string Optional. The description of the score explaining the directionality etc.
min	double Required. The minimum value of the score range (inclusive).
max	double Required. The maximum value of the score range (inclusive).
step	double Optional. The distance between discrete steps in the range. If unset, the range is assumed to be continuous.

MetricResult

Result for a single metric on a single instance.

Fields
rubric_verdicts[]	RubricVerdict Output only. For rubric-based metrics, the verdicts for each rubric.
score	float Output only. The score for the metric. Please refer to each metric's documentation for the meaning of the score.
explanation	string Output only. The explanation for the metric result.
error	Status Output only. The error status for the metric result.

MetricSource

The metric source used for evaluation.

Fields
Union field metric_source. The source of the metric. metric_source can be only one of the following:
metric	Metric Inline metric config.
metric_resource_name	string Optional. Resource name for registered metric.

MetricxInput

Input for MetricX metric.

Fields
metric_spec	MetricxSpec Required. Spec for Metricx metric.
instance	MetricxInstance Required. Metricx instance.

MetricxInstance

Spec for MetricX instance - The fields used for evaluation are dependent on the MetricX version.

Fields
prediction	string Required. Output of the evaluated model.
reference	string Optional. Ground truth used to compare against the prediction.
source	string Optional. Source text in original language.

MetricxResult

Spec for MetricX result - calculates the MetricX score for the given instance using the version specified in the spec.

Fields
score	float Output only. MetricX score. Range depends on version.

MetricxSpec

Spec for MetricX metric.

Fields
source_language	string Optional. Source language in BCP-47 format.
target_language	string Optional. Target language in BCP-47 format. Covers both prediction and reference.
version	MetricxVersion Required. Which version to use for evaluation.

MetricxVersion

MetricX Version options.

Enums
METRICX_VERSION_UNSPECIFIED	MetricX version unspecified.
METRICX_24_REF	MetricX 2024 (2.6) for translation + reference (reference-based).
METRICX_24_SRC	MetricX 2024 (2.6) for translation + source (QE).
METRICX_24_SRC_REF	MetricX 2024 (2.6) for translation + source + reference (source-reference-combined).

MigratableResource

Represents one resource that exists in automl.googleapis.com, datalabeling.googleapis.com or ml.googleapis.com.

Fields
last_migrate_time	Timestamp Output only. Timestamp when the last migration attempt on this MigratableResource started. Will not be set if there's no migration attempt on this MigratableResource.
last_update_time	Timestamp Output only. Timestamp when this MigratableResource was last updated.
Union field resource. resource can be only one of the following:
ml_engine_model_version	MlEngineModelVersion Output only. Represents one Version in ml.googleapis.com.
automl_model	AutomlModel Output only. Represents one Model in automl.googleapis.com.
automl_dataset	AutomlDataset Output only. Represents one Dataset in automl.googleapis.com.
data_labeling_dataset (deprecated)	DataLabelingDataset This item is deprecated! Output only. Deprecated: Data Labeling Dataset migration is no longer supported. Represents one Dataset in datalabeling.googleapis.com.

AutomlDataset

Represents one Dataset in automl.googleapis.com.

Fields
dataset	string Full resource name of automl Dataset. Format: projects/{project}/locations/{location}/datasets/{dataset}.
dataset_display_name	string The Dataset's display name in automl.googleapis.com.

AutomlModel

Represents one Model in automl.googleapis.com.

Fields
model	string Full resource name of automl Model. Format: projects/{project}/locations/{location}/models/{model}.
model_display_name	string The Model's display name in automl.googleapis.com.

DataLabelingDataset

Represents one Dataset in datalabeling.googleapis.com.

Fields
dataset	string Full resource name of data labeling Dataset. Format: projects/{project}/datasets/{dataset}.
dataset_display_name	string The Dataset's display name in datalabeling.googleapis.com.
data_labeling_annotated_datasets[]	DataLabelingAnnotatedDataset The migratable AnnotatedDataset in datalabeling.googleapis.com belongs to the data labeling Dataset.

DataLabelingAnnotatedDataset

Represents one AnnotatedDataset in datalabeling.googleapis.com.

Fields
annotated_dataset	string Full resource name of data labeling AnnotatedDataset. Format: projects/{project}/datasets/{dataset}/annotatedDatasets/{annotated_dataset}.
annotated_dataset_display_name	string The AnnotatedDataset's display name in datalabeling.googleapis.com.

MlEngineModelVersion

Represents one model Version in ml.googleapis.com.

Fields
endpoint	string The ml.googleapis.com endpoint that this model Version currently lives in. Example values: ml.googleapis.com us-centrall-ml.googleapis.com europe-west4-ml.googleapis.com asia-east1-ml.googleapis.com
version	string Full resource name of ml engine model Version. Format: projects/{project}/models/{model}/versions/{version}.

MigrateResourceRequest

Config of migrating one resource from automl.googleapis.com, datalabeling.googleapis.com and ml.googleapis.com to Agent Platform.

Fields
Union field request. request can be only one of the following:
migrate_ml_engine_model_version_config	MigrateMlEngineModelVersionConfig Config for migrating Version in ml.googleapis.com to Agent Platform's Model.
migrate_automl_model_config	MigrateAutomlModelConfig Config for migrating Model in automl.googleapis.com to Agent Platform's Model.
migrate_automl_dataset_config	MigrateAutomlDatasetConfig Config for migrating Dataset in automl.googleapis.com to Agent Platform's Dataset.
migrate_data_labeling_dataset_config (deprecated)	MigrateDataLabelingDatasetConfig This item is deprecated! Deprecated: Data labeling service is shut down. Config for migrating Dataset in datalabeling.googleapis.com to Agent Platform's Dataset.

MigrateAutomlDatasetConfig

Config for migrating Dataset in automl.googleapis.com to Agent Platform's Dataset.

Fields
dataset	string Required. Full resource name of automl Dataset. Format: projects/{project}/locations/{location}/datasets/{dataset}.
dataset_display_name	string Required. Display name of the Dataset in Agent Platform. System will pick a display name if unspecified.

MigrateAutomlModelConfig

Config for migrating Model in automl.googleapis.com to Agent Platform's Model.

Fields
model	string Required. Full resource name of automl Model. Format: projects/{project}/locations/{location}/models/{model}.
model_display_name	string Optional. Display name of the model in Agent Platform. System will pick a display name if unspecified.

MigrateDataLabelingDatasetConfig

Config for migrating Dataset in datalabeling.googleapis.com to Agent Platform's Dataset.

Fields
dataset	string Required. Full resource name of data labeling Dataset. Format: projects/{project}/datasets/{dataset}.
dataset_display_name	string Optional. Display name of the Dataset in Agent Platform. System will pick a display name if unspecified.
migrate_data_labeling_annotated_dataset_configs[]	MigrateDataLabelingAnnotatedDatasetConfig Optional. Configs for migrating AnnotatedDataset in datalabeling.googleapis.com to Agent Platform's SavedQuery. The specified AnnotatedDatasets have to belong to the datalabeling Dataset.

MigrateDataLabelingAnnotatedDatasetConfig

Config for migrating AnnotatedDataset in datalabeling.googleapis.com to Agent Platform's SavedQuery.

Fields
annotated_dataset	string Required. Full resource name of data labeling AnnotatedDataset. Format: projects/{project}/datasets/{dataset}/annotatedDatasets/{annotated_dataset}.

MigrateMlEngineModelVersionConfig

Config for migrating version in ml.googleapis.com to Agent Platform's Model.

Fields
endpoint	string Required. The ml.googleapis.com endpoint that this model version should be migrated from. Example values: ml.googleapis.com us-centrall-ml.googleapis.com europe-west4-ml.googleapis.com asia-east1-ml.googleapis.com
model_version	string Required. Full resource name of ml engine model version. Format: projects/{project}/models/{model}/versions/{version}.
model_display_name	string Required. Display name of the model in Agent Platform. System will pick a display name if unspecified.

MigrateResourceResponse

Describes a successfully migrated resource.

Fields
migratable_resource	MigratableResource Before migration, the identifier in ml.googleapis.com, automl.googleapis.com or datalabeling.googleapis.com.
Union field migrated_resource. After migration, the resource name in Agent Platform. migrated_resource can be only one of the following:
dataset	string Migrated Dataset's resource name.
model	string Migrated Model's resource name.

Modality

The modality of a Part of a Content message. A modality is the type of media, such as an image or a video. It is used to categorize the content of a Part for token counting purposes.

Enums
MODALITY_UNSPECIFIED	When a modality is not specified, it is treated as TEXT.
TEXT	The Part contains plain text.
IMAGE	The Part contains an image.
VIDEO	The Part contains a video.
AUDIO	The Part contains audio.
DOCUMENT	The Part contains a document, such as a PDF.

ModalityTokenCount

Represents a breakdown of token usage by modality.

This message is used in [CountTokensResponse][google.cloud.aiplatform.master.CountTokensResponse] and GenerateContentResponse.UsageMetadata to provide a detailed view of how many tokens are used by each modality (e.g., text, image, video) in a request. This is particularly useful for multimodal models, allowing you to track and manage token consumption for billing and quota purposes.

Fields
modality	Modality The modality that this token count applies to.
token_count	int32 The number of tokens counted for this modality.

Model

A trained machine learning Model.

Fields
name	string Identifier. The resource name of the Model.
version_id	string Output only. Immutable. The version ID of the model. A new version is committed when a new model version is uploaded or trained under an existing model id. It is an auto-incrementing decimal number in string representation.
version_aliases[]	string User provided version aliases so that a model version can be referenced via alias (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_alias} instead of auto-generated version id (i.e. projects/{project}/locations/{location}/models/{model_id}@{version_id}). The format is [a-z][a-zA-Z0-9-]{0,126}[a-z0-9] to distinguish from version_id. A default version alias will be created for the first version of the model, and there must be exactly one default version alias for a model.
version_create_time	Timestamp Output only. Timestamp when this version was created.
version_update_time	Timestamp Output only. Timestamp when this version was most recently updated.
display_name	string Required. The display name of the Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.
description	string The description of the Model.
version_description	string The description of this version.
default_checkpoint_id	string The default checkpoint id of a model version.
predict_schemata	PredictSchemata The schemata that describe formats of the Model's predictions and explanations as given and returned via PredictionService.Predict and PredictionService.Explain.
metadata_schema_uri	string Immutable. Points to a YAML file stored on Google Cloud Storage describing additional information about the Model, that is specific to it. Unset if the Model does not have any additional information. The schema is defined as an OpenAPI 3.0.2 Schema Object. AutoML Models always have this field populated by Agent Platform, if no additional metadata is needed, this field is set to an empty string. Note: The URI given on output will be immutable and probably different, including the URI scheme, than the one given on input. The output URI will point to a location where the user only has a read access.
metadata	Value Immutable. An additional information about the Model; the schema of the metadata can be found in metadata_schema. Unset if the Model does not have any additional information.
supported_export_formats[]	ExportFormat Output only. The formats in which this Model may be exported. If empty, this Model is not available for export.
training_pipeline	string Output only. The resource name of the TrainingPipeline that uploaded this Model, if any.
container_spec	ModelContainerSpec Input only. The specification of the container that is to be used when deploying this Model. The specification is ingested upon ModelService.UploadModel, and all binaries it contains are copied and stored internally by Agent Platform. Not required for AutoML Models.
artifact_uri	string Immutable. The path to the directory containing the Model artifact and any of its supporting files. Not required for AutoML Models.
supported_deployment_resources_types[]	DeploymentResourcesType Output only. When this Model is deployed, its prediction resources are described by the prediction_resources field of the Endpoint.deployed_models object. Because not all Models support all resource configuration types, the configuration types this Model supports are listed here. If no configuration types are listed, the Model cannot be deployed to an Endpoint and does not support online predictions (PredictionService.Predict or PredictionService.Explain). Such a Model can serve predictions by using a BatchPredictionJob, if it has at least one entry each in supported_input_storage_formats and supported_output_storage_formats.
supported_input_storage_formats[]	string Output only. The formats this Model supports in BatchPredictionJob.input_config. If PredictSchemata.instance_schema_uri exists, the instances should be given as per that schema. The possible formats are: jsonl The JSON Lines format, where each instance is a single line. Uses GcsSource. csv The CSV format, where each instance is a single comma-separated line. The first line in the file is the header, containing comma-separated field names. Uses GcsSource. tf-record The TFRecord format, where each instance is a single record in tfrecord syntax. Uses GcsSource. tf-record-gzip Similar to tf-record, but the file is gzipped. Uses GcsSource. bigquery Each instance is a single row in BigQuery. Uses BigQuerySource. file-list Each line of the file is the location of an instance to process, uses gcs_source field of the InputConfig object. If this Model doesn't support any of these formats it means it cannot be used with a BatchPredictionJob. However, if it has supported_deployment_resources_types, it could serve online predictions by using PredictionService.Predict or PredictionService.Explain.
supported_output_storage_formats[]	string Output only. The formats this Model supports in BatchPredictionJob.output_config. If both